Job intelligence
Data Scientist - Analytics Business Consulting
Lifelancer
Executive opportunity brief
The decision in one screen
A compact evidence-first read of compensation, flexibility, mobility and what still needs verification. No universal quality score is used.
Fast read
Opportunity DNA
There is a useful advantage here: location flexibility looks unusually broad. Still, the salary benchmark is still shallow could change the fit.
The range is usable, but the comparable sample is still too shallow for a strong relative-pay call.
No material geography, office-attendance or timezone restriction is visible in the available wording.
The listing does not make the minimum education or experience threshold clear enough yet.
Sponsorship and relocation support are not clear from the current listing.
Part of the application route can be assessed, but the full external process may contain additional steps.
At least one recent enabled source can still renew this vacancy.
This fingerprint summarizes what is clear, constrained or still unanswered in the current listing. It is a reading aid, not an overall job score.
Decision evidence check
5 of 5 core facts stated
This checks whether the listing states the facts that commonly change an employment decision. It measures disclosure completeness, not whether the opportunity is good or bad.
Facts already stated
- Compensation
- Work arrangement
- Advertised location
- Application closing time
- Employment type
Core evidence complete
All five core facts are stated. Other details can still matter, so continue with the qualification, mobility, employer and application checks below.
Advertised location is not proof of work authorization, sponsorship, or legal eligibility. Unknown information is not a negative score; verify those questions separately.
Decision levers
What could make or break the fit?
Reasons to look closer
- Location flexibility looks unusually broad: The available wording supports high Remote Freedom without a material geography, office-attendance or timezone restriction showing up.
Trade-offs to weigh
- The salary benchmark is still shallow: Pay is disclosed, but the current comparison pool is too small for a strong relative-pay conclusion.
Questions worth asking before you invest more time
- Confirm whether a degree is actually required, preferred, or replaceable with equivalent experience.
- Confirm the actual minimum experience requirement rather than assuming preferred background is mandatory.
- Ask whether visa sponsorship is available before investing significant time in the application.
These questions come from details the listing leaves open. An unanswered point is not treated as a disadvantage.
Opportunity neighborhood
Keep your options open
These live roles sit close enough to this opportunity to make useful comparison points. Look across several employers before deciding what pay, flexibility or access trade-offs you are willing to accept.
Data Analyst
Naples Jet Center · Remote — Worldwide
Why it is nearby: Same seniority band · Similar work arrangement · Same employment type
Data Analyst - Cardio Partners - Remote
Sarnova · Remote — Worldwide
Why it is nearby: Same seniority band · Similar work arrangement · Same employment type
AI Data Scientist
Airtm · LATAM
Why it is nearby: Similar work arrangement · Same employment type · Recently posted
Different angle: Seniority is not explicit
Machine Learning Engineer - Inference Maintainer & Developer Experience
Roboflow · USA
Why it is nearby: Similar work arrangement · Same employment type · Recently posted
Different angle: Seniority is not explicit
This neighborhood is drawn from current inventory in the same broad occupation. Similarity is based on explicit listing facts; missing details do not count against a job.
Role market context
How much current choice is visible around this kind of work?
This compares the vacancy with current High Paid Job inventory in the broad Data & AI roles family. It describes visible choice and disclosure, not future demand or career quality.
200 most recent current roles sampled for the disclosure measures below.
Distinct employers in the bounded current sample, not an estimate of all employers hiring in the market.
Current sampled roles stating at least four of the five Core Decision Facts.
Requires a numeric amount, explicit ISO currency and supported pay period.
Listings with explicit enough remote, hybrid or on-site evidence to classify.
Missing deadline evidence remains unknown and is not interpreted as an open-ended application window.
Currencies with explicit pay evidence in the sample: USD (35) · EUR (18) · GBP (3) · CAD (2). Raw amounts across different currencies are not compared.
This describes current visible High Paid Job inventory in the same broad occupation family. Percentages use a bounded recent sample and are not a forecast, a complete labor-market census or an employer-quality measure.
Constraint opportunity lab
What changes the size of the current opportunity pool?
Each line applies one stricter evidence-based constraint to the same bounded current role-family sample. It helps you see the trade-off in visible choice without deciding which trade-off is right for you.
Counts only affirmative or possible sponsorship evidence, or relocation explicitly offered. Unknown is not treated as no.
A much stricter subset: only listings with explicit worldwide-remote evidence remain.
Requires amount, explicit ISO currency and a supported pay period. Missing pay evidence remains unknown.
Shows the subset with enough stated evidence for a more informed first comparison.
Counts roles explicitly classified as worldwide remote, region-restricted remote or remote with restrictions still unclear.
Includes roles whose seniority is genuinely unspecified rather than assuming they belong to another level.
Only same-currency opportunities with usable annualized pay enter this comparison. Other currencies are not converted or treated as lower.
Uses only roles with a stated employment type.
These are counterfactual filters over a bounded sample of current HPJ inventory in the same broad occupation family. They show how visible choice changes when one evidence-based constraint is applied; they do not tell you which constraint you should choose or predict hiring outcomes. The calculation uses the 200 most recent current roles from 1213 visible roles in this broad family.
Salary transparency context
Is pay disclosure typical for this kind of role?
This listing states usable structured compensation. About 29% of the bounded current role sample does the same.
Compensation counts only when amount, ISO currency and supported pay period are all known.
Pay disclosure is limited in the current role sample.
This employer’s current visible listings disclose pay materially more often than the current same-role sample.
This compares salary-disclosure behavior in current visible listings. It does not compare raw salary amounts across currencies, infer undisclosed pay as zero, or rate employer quality.
Private offer & negotiation lab
Turn an offer into evidence, not guesswork
Enter an annual base offer and, if you want, your own target. HPJ compares only within one explicit currency and never chooses a target for you.
Negotiation evidence available: advertised pay range, current same-currency market context, strong decision evidence.
Enter an annual base offer to compare it with explicit same-currency evidence. HPJ will not suggest a target for you.
Review the full package · 10 terms
Mark what is confirmed, what needs clarification and what you want to prioritize. These choices and notes stay in this browser.
Usable advertised compensation is available.
Variable compensation is mentioned; verify amount, formula, target and eligibility.
Equity or long-term incentive language is mentioned; verify grant type, vesting and valuation basis.
Retirement or pension benefits are mentioned; verify employer contribution and vesting.
Health-benefit language is mentioned; verify coverage, dependants and employee cost.
Paid-leave language is mentioned; verify the exact entitlement and carry-over rules.
A work-arrangement signal is available; verify schedule details before relying on it.
Relocation support not established
Sponsorship not established
A review cycle is mentioned; verify timing and whether compensation can change at that review.
Questions worth taking into the offer conversation
- What retirement or pension contribution does the employer provide?
- How much paid leave is provided and what are the carry-over or blackout rules?
- If relocation is relevant, what expenses, timing and repayment conditions are covered?
- What exact sponsorship or work-authorization conditions apply to this role?
- When is the first performance and compensation review after joining?
Occupation intelligence
What broader career references connect to this role?
HPJ uses broad comparison families for current-market analysis. External taxonomy results are reference candidates for that broad family, not a declaration that this vacancy has been authoritatively classified into a specific occupation.
European occupation references
This service uses the ESCO classification of the European Commission.
U.S. occupation references
- Data Scientists 15-2051.00
- Computer and Information Research Scientists 15-1221.00
- Social Science Research Assistants 19-4061.00
- Atmospheric and Space Scientists 19-2021.00
- Bioinformatics Scientists 19-1029.01
This site incorporates information from O*NET Web Services by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). O*NET® is a trademark of USDOL/ETA.
Career reference research
Skills worth investigating
Official occupational context, paired with exact phrases visible in this vacancy. It is not a skills test or an applicant-fit score.
ESCO · European occupation
data scientist
Usually essential in this ESCO occupation
- statisticsPhrase in listing
- think abstractly
- apply research ethics and scientific integrity principles in research activities
- integrate gender dimension in research
- promote the participation of citizens in scientific and research activities
- communicate with a non-scientific audience
- implement data quality processes
Context-dependent ESCO skills
- Hadoop
- unstructured data
- apply blended learning
- teach in academic or vocational contexts
- digital curation
O*NET · U.S. occupation
Data Scientists
O*NET occupation skills
- MathematicsPhrase in listing
- Critical Thinking
- Reading Comprehension
- Complex Problem Solving
- Programming
- Active Learning
- Speaking
- Active Listening
Build my private skill preparation brief Your evidence · Practice · Interview questions
Plan how you might demonstrate or investigate occupational skills. These are broad-family reference examples, not confirmed vacancy requirements. Nothing you enter is sent to High Paid Job.
Private to this browser. Up to 20 reference skills; not a qualification assessment or hiring prediction.
Useful next question: Which of these occupational reference skills does the hiring manager actually expect for this specific role, and what evidence would demonstrate them?
These skills belong to one reference occupation selected for HPJ’s broad career family, not a verified classification of this vacancy. Text overlap only means a phrase appears in the available listing text; it does not establish an employer requirement, your proficiency, or a qualification gap. A missing mention is unknown, not a deficit.
Adjacent current roles to investigate
Automationsentwickler / RPA Developer (m/w/d) – UiPath | Energiebranche | 99 % R
KLAR-Franchise GmbH · Remote — Worldwide
same seniority band · same employment type · similar work-arrangement evidenceFull-Stack Engineer (Backend Leaning) - Studio
ElevenLabs · Remote
same employment type · similar work-arrangement evidenceFront Office, Product Owner
Derivative Path · Remote — Worldwide
same employment type · similar work-arrangement evidenceAdjacent-family roles are exploration prompts from current HPJ inventory. They are not inferred qualification matches and they do not imply a promotion path.
Career move map
What sits one step away?
This listing is currently read as Mid-career. Nearby roles one visible seniority step away can help you judge whether to widen the search, stretch upward or compare the responsibilities more carefully.
Broader-entry direction
Data Engineer
EdgeQ Technologies Private Limited · Remote — Worldwide
Similar work arrangement · Same employment type · Recently postedGraduate / Junior Data Scientist
Thgingenuity · Manchester
Recently postedStretch direction
Senior Data Engineer
InTalent Asia · Remote — Worldwide
Similar work arrangement · Same employment type · Recently postedMachine Learning Scientist, Multimodal AI
Natera · USA
Similar work arrangement · Same employment type · Recently postedThese are live roles one visible seniority step away in the same broad occupation family. They can reveal nearby options, but they are not a guaranteed promotion path and do not account for your personal experience.
Skill bridge
What does this role connect to?
AWS is one of the signals this listing shares with skills appearing often across current Data & AI roles opportunities.
Skills named in this listing
Shared with the current career sample
- AWS Seen in 33 sampled listings
- Machine Learning Seen in 48 sampled listings
- Python Seen in 89 sampled listings
- SQL Seen in 78 sampled listings
Combinations also appearing together
- Python + SQL appeared together in 66 sampled listings
- Machine Learning + Python appeared together in 38 sampled listings
- AWS + Python appeared together in 27 sampled listings
Application edge
- If these skills match your background, prepare one concrete example that shows how you used AWS, Machine Learning, and Python rather than relying on a keyword-only claim.
- The listing mentions extra application steps. Review those requirements before starting so you can prepare the requested material in one pass.
If the named skills are genuinely transferable in your own experience, the occupation map also points to adjacent current families worth researching: Software engineering roles and Product roles. This is exploration context, not a qualification match.
Skill signals come from explicit text mentions. A mention is not automatically a requirement, and absence from the current top-skill list does not make a skill rare or unimportant. The current career comparison draws on 120 sampled listings; 117 contain a recognized skill signal.
Application Decision Lab
Turn the listing into a preparation plan
Separate what the listing actually states from what you still need to prepare or verify. Your selections and short notes stay in this browser and are never submitted to High Paid Job.
AWS
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
Machine Learning
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
Python
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
SQL
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
Prepare for a stated application step
An assessment, test or work-sample step is explicitly mentioned.
Prepare for a stated application step
A case study, presentation or case-style exercise is explicitly mentioned.
Questions worth resolving before you rely on the opportunity
- Work authorization / sponsorship: If sponsorship or work authorization matters to you, what exact immigration or employer-sponsorship conditions apply to this role?
Decision analysis
What the listing really tells you
The headline alone does not tell the whole story for Data Scientist - Analytics Business Consulting at Lifelancer. The clearest decision context comes from pay is visible, but the benchmark is still thin, the location rules are clear enough to judge, and the visible application effort can be estimated.
Pay is visible, but the benchmark is still thin
A usable EUR salary is available, but there are not enough comparable current observations for a strong percentile statement yet.
The location rules are clear enough to judge
Location flexibility can be assessed here: Highly location-flexible, with a Remote Freedom reading of 96/100.
The visible application effort can be estimated
Provider-mediated application path · 2 visible process steps. The external application may contain additional steps that are not visible from the listing itself.
The listing can be traced to a current source
1 recent enabled source can currently renew this vacancy across 1 source classes. That improves traceability, although it does not verify the employer itself.
How this analysis is assembled
This read combines the structured details currently available for pay, flexibility, qualifications, mobility, application effort and source continuity. Missing facts are left open for you to verify.
Analysis evidence coverage
How much analysis can the current evidence support?
Only 1 comparable salary observations are available. At least 10 are needed before showing a High Pay Score.
Measures observable location flexibility and explicit restrictions; it does not determine whether remote work is personally desirable.
The salary is source-provided and normalizable, but the advertised range is broad enough to reduce comparison precision.
The source posting date is today.
At least one enabled source recently observed the vacancy. The source chain is traceable, but this is not independent employer verification.
There is no universal opportunity score because pay, flexibility and access matter differently to different people. Compare Jobs lets you weight the evidence around your own priorities.
Decision checklist
What is supported, uncertain or worth checking?
This checklist separates supported strengths, visible cautions and missing evidence. It is not an overall recommendation or guarantee that the job is suitable for you.
Supported strengths
- The available work-location evidence indicates strong location flexibility.
Visible cautions
No specific caution is supported by the currently available evidence. This does not prove that none exists.
Still unknown
- There is not enough comparable salary evidence to establish this job’s current pay position.
- Visa sponsorship is not established by the available listing data.
Pay vs. barriers
Opportunity Efficiency not assessed
The current pay position is not reliable enough to weigh compensation against the stated barriers.
- Current comparable pay position is unavailable.
- Education requirement is not established.
- Required prior-experience level is not established.
This section weighs pay context against clearly stated barriers without collapsing unlike requirements into one universal score. Degree, experience, licensing, clearance and location limits remain separate, and missing qualification details stay unknown.
Beyond the headline
Not enough evidence for a hidden-value finding
Potential hidden value appears only when several distinct non-pay advantages are actually supported. Missing requirements or benefits are never counted as positives.
The observable Remote Freedom evidence is strong and contains few explicit restriction signals.
Still check: The current application path is provider-mediated rather than directly classified as an employer-authorized or official route.
Hidden Value is an experimental evidence summary, not an investment-style score or guarantee of employability. It only uses explicit current listing evidence and does not infer that an unmentioned qualification is unnecessary.
Explainable opportunity highlight
Why this opportunity stands out
These are supported signals from the current listing and comparison sample—not a guarantee that this is the best job for you.
The observable work-location signals indicate unusually strong location flexibility.
The source posting date is today.
Compensation intelligence
How competitive is this pay?
Only 1 comparable salary observations are available. At least 10 are needed before showing a High Pay Score.
Why this analysis?
- Only 1 comparable salary observations are available. At least 10 are needed before showing a High Pay Score.
- Salary confidence: Medium. The salary is source-provided and normalizable, but the advertised range is broad enough to reduce comparison precision.
- Range quality: Broad but usable range.
Employer compensation communication
Pay transparency at Lifelancer
Mixed current pay transparency
This score uses the employer’s current visible listings and combines how often pay is disclosed with how useful those ranges are. Bonus, equity and benefit mentions are shown separately and do not raise the score.
This pattern comes from the employer’s current visible vacancies. It describes hiring and pay transparency in those listings, not employee satisfaction or a permanent judgement of the employer.
Current employer intelligence
Lifelancer — current opportunity snapshot
Employer Evidence Profile
How often this employer’s current visible sampled vacancies state decision-critical information. These percentages describe evidence coverage, not employer quality.
Current sampled listings with advertised compensation evidence.
Current sampled listings whose work arrangement is clearer than unknown.
Current sampled listings with a supported country signal.
Current sampled listings with an explicit closing time.
Current sampled listings with a usable posting date.
Current sampled listings that explicitly indicate confirmed, possible or unavailable sponsorship evidence.
Current sampled listings with an explicit relocation signal.
Low coverage means the current visible listings state the information less often. It does not prove that the employer lacks the policy, benefit or support.
Common current occupation families: Healthcare (13) · Sales (4) · Operations (3)
Visible seniority mix: Early-career (3) · Mid-career (14) · Senior / lead (11) · Leadership (7)
Current employment-type mix: Full time (35) · Contract (1)
Other current roles from this employer
Regional Clinical Trial Management Associate
Remote — Worldwide
Mid-career · Worldwide remoteCustomer Success Manager - Quality
Remote — Worldwide
Senior / lead · Worldwide remoteClinical Independent Rater - Romanian Speaker
Remote — Worldwide
Mid-career · Worldwide remoteSenior Safety Domain Expert - Falcon
Remote — Worldwide
Senior / lead · Worldwide remoteThese are other current vacancies visible from the same employer, not endorsements or evidence of future hiring plans.
Current disclosed salary medians by currency
- USD 141,130 median · 20 disclosed salaries
Questions worth asking this employer
- If work authorization matters to you, what sponsorship policy applies to this specific role and location?
- If a move is required, is relocation assistance available for this position?
These questions are prompted by missing or thin evidence in the employer’s current visible vacancies. Missing evidence is not treated as a negative judgement.
This employer snapshot uses the employer’s current visible listings. Remote and sponsorship rates use only vacancies with explicit evidence, while missing information stays unknown. Salary medians are shown separately by currency and need at least five disclosed salaries. Zero confirmed sponsorship means none was explicitly found in this sample, not that sponsorship is unavailable.
This snapshot describes the employer’s current visible vacancies—things such as pay disclosure and work arrangement. It is not an employee-satisfaction or hiring-quality rating, and different currencies stay separate.
Work-location intelligence
Remote Freedom
Highly location-flexible
Why this score?
- The source describes the role as open worldwide or globally.
Flexibility signals detected
- Worldwide or global remote eligibility is explicitly indicated.
Remote Freedom measures observable location flexibility only. Unknown work-location information remains unscored. Timezone wording is penalized only when it appears restrictive; general collaboration across time zones is not treated as a restriction. A score does not guarantee legal work eligibility, payroll availability or visa sponsorship.
International applicant intelligence
Visa & relocation signals
Sponsorship status is shown only when the listing provides clear enough wording. Employer history or headquarters location is not used to guess an answer.
The available listing details do not contain a clear enough sponsorship statement.
The available listing details do not contain a clear enough relocation statement.
Tradeoff check
What’s the catch?
No obvious trade-off appears in the available job details. That does not mean the role has no drawbacks, so the original listing and employment terms are still worth reading carefully.
Current alternatives
Better-paying alternatives
These current roles show a materially higher annualized salary midpoint in the same broad occupation and currency. Similar seniority, country, work arrangement, employment type and recency are preferred where possible; higher pay alone does not make an alternative a better fit.
AI-First Data Engineer (m/f/d)
Sport Alliance GmbH · Remote (Germany)
UnclearWhy comparable: Same employment type · Recently posted
Check: Seniority is not explicit in the alternative listing.
AI-First Analytics Engineer (m/f/d)
Sport Alliance GmbH · Remote (Germany)
UnclearWhy comparable: Same employment type
Check: Seniority is not explicit in the alternative listing.
Alternant(e) Expert en Ingénierie des Données / Data Engineer H/F
kaischool · Fontenay-sous-Bois
On-site / hybridWhy comparable: Recently posted
Check: Seniority is not explicit in the alternative listing.
Alternatives are drawn from the current inventory rather than presented as application recommendations. A pay difference needs to be meaningful—about 3% or more at the midpoint—and different currencies are not compared.
Application-path intelligence
What can we see about the application process?
Provider-mediated application path
The current application path comes through a publisher or aggregator. The destination may still lead to an employer-authorized hiring system, but that is not clear enough to assume.
Current destination: himalayas.app · HTTPS
Visible application steps or requirements
- An assessment, test or work-sample step is explicitly mentioned.
- A case study, presentation or case-style exercise is explicitly mentioned.
Application effort is estimated from visible listing and source details. Optional steps are kept separate from mandatory ones, and the external form may contain additional requirements that are not visible here.
Application safety context
Application safety check
Basic application-safety context available
The current application destination has a basic transport-safety signal such as HTTPS. This does not verify the employer, vacancy, recruiter or application process.
Evidence checked: Source provenance · Application destination · Listing wording
Observable transport-safety signals
- The available application destination uses HTTPS.
Before you share money or sensitive information
- Confirm the vacancy on the employer’s official careers site when practical, especially if a recruiter contacts you outside the listed application path.
- Check that recruiter email domains and application destinations are consistent with the employer or an identifiable recruiting provider.
- Do not pay application, processing, training or equipment fees merely to be considered for a job.
- Do not send cryptocurrency, gift cards, online-banking credentials or PINs as part of an application.
- Treat unusually fast offers or interviews conducted only through informal messaging channels as reasons to verify the employer independently.
This is a limited safety screen, not employer verification and not a fraud determination. Never send money, cryptocurrency, passwords or sensitive banking credentials merely because a listing appears on a job platform.
Original listing details
The section below comes from the listing source. The decision sections above use the job’s structured pay, location, qualification, mobility and application details to give you additional context.
Job Title: Data Scientist - Analytics Business Consulting
Job Location: Barcelona, Spain
Job Location Type: Remote
Job Contract Type: Full-time
Job Seniority Level:
Veeva is building the industry cloud for Life Sciences through software, data, AI, and business consulting working together. Learn more about our products, vision, values, and status as a Public Benefit Corporation (PBC) on our website.
Consulting is changing, and we’re not like other firms. Launched in 2019, Veeva Business Consulting has grown quickly and organically to over 500 global experts who combine deep domain knowledge with innovative technology to help Life Sciences companies work in a more efficient and connected way.
We are growing aggressively as we look towards 2030. We are seeking great people to help shape the future of the industry with the world’s leading software, data, AI, and expertise at their back.
The Role
As a Data Scientist in our EU Analytics practice, you will help customers bring treatments to patients faster by transforming how they work with Technology, Data, and AI. You will focus on reshaping operating models, driving organizational change, and measuring tangible business value.You will play a pivotal role in building advanced analytical models and methodologies, integrating Veeva Data Cloud products with complex European healthcare data assets. You will drive innovation across our analytical offerings, ensuring Veeva remains at the forefront of data-led, tech-enabled Life Sciences commercial models. You will also collaborate closely with cross-functional teams to bridge technical modeling with practical business insights across European markets. You will take ownership of any of our analytics assets (e.g., design solution, manage expectations, status accountancy, impact measurement).What You'll Do
- Design, prototype, and scale advanced analytics solutions for Life Sciences customers across European markets
- Process, harmonize, and connect Veeva Data Cloud products with diverse European healthcare data assets
- Develop and apply statistical methodologies, machine learning, and AI models to solve complex, high-impact commercial and strategic business questions in the Life Sciences space
- Build and design HCP precision engagement engines that connect disparate healthcare datasets integrated seamlessly with customer CRM information to enable personalized, data-driven targeting and activation
- Collaborate closely in integrated, cross-functional teams alongside product managers, data engineers, operations, and consultants to deliver end-to-end analytical solutions and ensure continuous feedback and iteration
- Maintain clear documentation of data methodologies, model architectures, and validation processes to ensure defensibility, scalability, and transparency across client engagements
Requirements
- 2+ years of proven experience in data science, advanced analytics, or machine learning roles, preferably within Life Sciences, pharmaceuticals, or healthcare analytics
- Strong problem-solving and communication skills, with a track record of collaborating effectively across cross-functional teams. Skilled at translating complex technical challenges to non-technical stakeholders, driving impactful problem-solving sessions, and championing a pragmatic approach to solution design
- Strong technical expertise across Python, SQL, advanced analytics, machine learning, and AI, with a proven ability to design and implement predictive models and intelligent algorithms that solve complex business problems
- You bring Experience writing process documentation, technical specifications, and data collection guidelines to ensure quality, scalability, and repeatability across projects
Nice to Have
- Knowledge of Veeva products
- Experience delivering SaaS or Tech-enabled consulting solutions
- M.S. or Ph.D. in Computer Science, Statistics, Data Science, Mathematics, or a related quantitative field
- Languages in addition to English
- Understanding of healthcare, life sciences, or pharmaceutical industry data
- Experience with data visualization tools to create dashboards and reports
- Understanding of data engineering concepts (ETL, data pipelines) and automation tools such as Apache Airflow
- Familiarity with cloud based computing resources such as AWS
Interviewing with Veeva
We value your time and believe in a transparent hiring process. Here is the process you can expect.
- Follow the application process and submit your resume.
- Within 3 days, you will receive a link to a personality assessment administered by a third party.
- Once you complete the assessment, our team will review your full application package and follow up via email with our decision.
- If moving to the interview stage, the process is as follows:
- A conversation with the hiring manager
- A practical case exercise
- A final conversation with our group's Senior Leader.
- Once all interviews are complete, the manager will be in touch with a final decision.
Perks & Benefits
- Compensation: Highly competitive salary and equity in Veeva
- Growth: Unrivalled career progression in a fast-growing, global team
- Flexibility: Veeva is a Work Anywhere company. You can work from home or the office on any given day
Compensation
- Total compensation: 48,000 - 75,000 EUR
- The total compensation range listed here has been provided to comply with local regulations and represents a potential total compensation range for this role. Please note that actual total compensation may vary within the range above or below, depending on experience and location. We look at compensation for each individual and base our offer on your unique qualifications, experience, and expected contributions. This position's total compensation may be composed of other types of compensation, such as variable bonus and/or stock bonus.
This job is curated by Lifelancer.
Lifelancer is a talent-hiring platform in Life Sciences, Pharma and IT. The platform connects talent with opportunities in pharma, biotech, health sciences, healthtech and IT domains.
Please apply via Lifelancer platform to get connected to the application page and to find similar roles.
Originally posted on Himalayas
Listing provenance
Where did this job come from?
See where this vacancy came from and whether more than one current source can still support it. Source names help with traceability and attribution; they are not endorsements or guarantees.
At least one enabled source recently observed the vacancy. The source chain is traceable, but this is not independent employer verification.
- Continuity
- One recent renewable source
- Source-class independence
- One renewable source class
- Last source observation
- Observed today
This records when High Paid Job last observed the vacancy in source evidence. It confirms source presence, not that the employer edited the vacancy at that time.
- 1 enabled source recently observed this vacancy.
Source confidence is about traceability and continuity. It is separate from application safety and should not be read as an employer reputation rating.
The vacancy is supplied by an external job publisher. Source information helps show where the listing came from; it is not a guarantee about the employer or vacancy.
Listing source: Himalayas. Destination: himalayas.app. The application opens in a new tab so you can keep this report open.