Job intelligence
Senior Data Scientist
Qventus
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
The role stands out because location flexibility looks unusually broad. Before going further, confirm whether a degree is actually required, preferred, or replaceable with equivalent experience.
Current comparable position is around the 35th percentile.
No material geography, office-attendance or timezone restriction is visible in the available wording.
Some access and qualification signals are clear, but the overall entry path is neither unusually open nor unusually restrictive.
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.
Current renewable source evidence is limited, so confirm the vacancy is still open before sharing sensitive information.
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
No major trade-off is stated clearly in the information available here. Employment terms outside the listing may still matter.
Questions worth asking before you invest more time
- Confirm whether a degree is actually required, preferred, or replaceable with equivalent experience.
- Ask whether visa sponsorship is available before investing significant time in the application.
- Verify that the vacancy is still active on the authoritative employer or application destination before sharing sensitive information.
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 Scientist, Enterprise Analytics
GitLab · Canada, USA
Why it is nearby: Same seniority band · Similar work arrangement · Same employment type
About 12% lower advertised midpoint
Lead AI Engineer
Fever · Spain
Why it is nearby: Same seniority band · Similar work arrangement · Same employment type
Business Intelligence Professional
Trimble · Remote — Worldwide
Why it is nearby: Same seniority band · Similar work arrangement · Same employment type
Senior Data Analyst
Fusemachines · Remote — Worldwide
Why it is nearby: Same seniority band · Similar work arrangement · Recently posted
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 (37) · EUR (18) · GBP (6) · CHF (1). 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.
Counts roles explicitly classified as worldwide remote, region-restricted remote or remote with restrictions still unclear.
Shows the subset with enough stated evidence for a more informed first comparison.
Only same-currency opportunities with usable annualized pay enter this comparison. Other currencies are not converted or treated as lower.
Includes roles whose seniority is genuinely unspecified rather than assuming they belong to another level.
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 1271 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 32% 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?
- 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
- handle data samples
- integrate gender dimension in research
- deliver visual presentation of data
- scientific literature
- promote the participation of citizens in scientific and research activities
- perform data cleansing
- manage intellectual property rights
Context-dependent ESCO skills
- MDX
- scientific computing
- digital curation
- integrate ICT data
- state estimation
O*NET · U.S. occupation
Data Scientists
O*NET occupation skills
- Mathematics
- 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
Senior Software Developer Go | Python | Backend & Microservices
Göteborg · GÖTEBORG, Västra Götalands län, Sverige
same seniority band · same employment typeLead Data Product Architect
Nordic Investin Group · Stockholm, Stockholms län, Sverige
same seniority band · same employment typeSenior MLOps Platform Engineer
Nordic Investin Group · Stockholm, Stockholms län, Sverige
same seniority band · same employment typeAdjacent-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 Senior / lead. 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
Associate Data Scientist
Jerry · Remote — Worldwide
Similar work arrangement · Same employment type · Recently posted Advertised midpoint is about 17% lowerBusiness Intelligence Engineer III, Workday and AI Copilot
Sutter Health · Remote — Worldwide
Similar work arrangement · Same employment type · Recently posted Advertised midpoint is about 2% lowerStretch direction
Marketing & GTM Data Analyst
Deepgram · USA
Similar work arrangement · Same employment type · Recently posted Advertised midpoint is about 11% lowerLead Data Analyst
Liftoff · USA
Similar work arrangement · Same employment type · Recently posted Advertised midpoint is about 12% higherThese 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 27 sampled listings
- Databricks Seen in 24 sampled listings
- Large Language Models Seen in 33 sampled listings
- Machine Learning Seen in 50 sampled listings
Combinations also appearing together
- Python + SQL appeared together in 47 sampled listings
- Machine Learning + Python appeared together in 28 sampled listings
- AWS + Python appeared together in 23 sampled listings
Additional signals in this listing: Generative AI
Application edge
- If these skills match your background, prepare one concrete example that shows how you used AWS, Databricks, and Generative AI rather than relying on a keyword-only claim.
- The listing states about 1 year of required experience. Check that the dates and scope in your application make that experience easy to verify.
- 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; 119 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.
At least about 1 year of experience required
The available wording contains an explicit minimum/required years-of-experience signal. Preferred years are tracked separately and never promoted into the required minimum.
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.
Databricks
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.
Generative AI
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.
Large Language Models
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.
Prepare for a stated application step
A drug or substance screening step 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
For Senior Data Scientist at Qventus, the most useful clues right now are compensation has enough depth for comparison, the location rules are clear enough to judge, and the listing gives useful clues about who can realistically enter. Looking at them together makes the trade-offs easier to see.
Compensation has enough depth for comparison
Among 93 current comparable salaries in USD, the normalized annual midpoint is around the 35th percentile. This compares advertised pay in the same currency and does not include every element of total compensation.
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 listing gives useful clues about who can realistically enter
At least about 1 year of experience required · 1 additional stated barrier. Unstated education or experience requirements stay open rather than being assumed.
The application route gives a few useful clues
Application path not classified · 1 visible process step. The external application may contain additional steps that are not visible from the listing itself.
How this analysis is assembled
Built from the pay, location, qualification, mobility, application and source details available for this vacancy. Anything the listing leaves unresolved stays unresolved.
Analysis evidence coverage
How much analysis can the current evidence support?
Uses the explainable High Pay Score within the job’s current occupation/seniority/currency comparison sample.
Measures observable location flexibility and explicit restrictions; it does not determine whether remote work is personally desirable.
The configured source supplied structured salary fields, a usable currency and a focused comparison value.
The listing was posted about 35 days ago. Verify that it is still accepting applications.
There is not enough source history yet to describe how well this listing can be traced across current feeds.
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
- The listing was posted about 35 days ago. Verify that it is still accepting applications.
- The description appears to mention an advanced degree requirement or preference.
- At least about 1 year of experience required
Still unknown
- Visa sponsorship is not established by the available listing data.
- Current source provenance is insufficient for a stronger traceability assessment.
- The directness of the application route is not established.
Pay vs. barriers
Pay and barrier context available
The current pay position can be viewed alongside the stated barriers, but the available detail does not support a stronger conclusion.
Comparable disclosed-pay evidence places this job at about 35 / 100 within its current occupation/seniority sample.
1 explicit required barrier is visible in the available wording.
The listing states at least about 1 year of required experience.
- Education requirement 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: At least about 1 year of experience required
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 source provides structured salary information with a comparatively focused range, improving the usefulness of pay comparisons.
The observable work-location signals indicate unusually strong location flexibility.
Compensation intelligence
How competitive is this pay?
Below many comparable disclosed salaries
The annualized midpoint is below the current comparable median but remains within the sample’s lower-to-middle range.
The highlighted band is the middle 50% of the current comparable sample after the outlier guard. The dark marker is this job’s annualized salary midpoint.
Why this analysis?
- This score reflects the current advertised-pay distribution for comparable active jobs in the same currency. It measures pay position, not overall job quality.
- Salary confidence: High. The configured source supplied structured salary fields, a usable currency and a focused comparison value.
- Range quality: Focused salary range.
Employer compensation communication
Pay transparency at Qventus
Very strong 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
Qventus — 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: Customer success (1) · Sales (1) · Executive (1)
Visible seniority mix: Senior / lead (1) · Leadership (4)
Current employment-type mix: Full time (5)
Most visible hiring countries in this sample: United States (3)
Other current roles from this employer
Director, Client Success
USA
Leadership · Region-restricted remoteBusiness Development Manager
USA
Leadership · Region-restricted remoteAI Tooling Engineer
USA
Leadership · Region-restricted remoteDirector, Implementation Success
Remote — Worldwide
Leadership · 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 150,000 median · 5 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?
- What is the expected application and interview timeline if the listing does not state a closing date?
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?
These are visible conditions worth checking before treating the headline compensation as the whole story:
- The description appears to mention an advanced degree requirement or preference.
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.
Principal Data Scientist, User Ecosystem
Reddit · USA
Region-restricted remoteWhy comparable: Same seniority band · Similar work-location arrangement · Same employment type
Engineering Manager, Machine Learning (Safety)
Discord · USA
Region-restricted remoteWhy comparable: Same seniority band · Similar work-location arrangement · Same employment type
Staff Machine Learning Engineer, Home Surfaces
Spotify · Canada, USA
Region-restricted remoteWhy comparable: Same seniority band · Similar work-location arrangement · Same employment type
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?
Application path not classified
The available provenance is not specific enough to characterize how direct the application route is.
Visible application steps or requirements
- A drug or substance screening step 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
Limited application-safety evidence
There is not enough observable application-safety evidence to make a stronger statement. Missing evidence is not treated as a negative signal, and source traceability is evaluated separately.
Evidence checked: Listing wording
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.
On this journey for over 12 years, Qventus is leading the transformation of healthcare. We enable hospitals to focus on what matters most: patient care. Our innovative solutions harness the power of machine learning, generative AI, and behavioral science to deliver exceptional outcomes and empower care teams to anticipate and resolve issues before they arise.
Our success in rapid scale across the globe is backed by some of the world's leading investors. At Qventus, you will have the opportunity to work with an exceptional, mission-driven team across the globe, and the ability to directly impact the lives of patients. We’re inspired to work with healthcare leaders on our founding vision and unlock world-class medicine through world-class operations.
Job Description
About Us
On this journey for over 12 years, Qventus is leading the transformation of healthcare. We enable hospitals to focus on what matters most: patient care. Our innovative solutions harness the power of machine learning, generative AI, and behavioral science to deliver exceptional outcomes and empower care teams to anticipate and resolve issues before they arise.
Our success in rapid scale across the globe is backed by some of the world's leading investors. At Qventus, you will have the opportunity to work with an exceptional, mission-driven team across the globe, and the ability to directly impact the lives of patients. We’re inspired to work with healthcare leaders on our founding vision and unlock world-class medicine through world-class operations.
About the Role
We’re looking for an experienced Data Scientist to join our Data Science team. In this role, you will leverage your expertise to develop and implement cutting edge solutions leveraging a wide range of machine learning, statistical, and other mathematical optimizations. You will evaluate potential modeling approaches, implement and operationalize algorithms with your fellow data scientists and product teams, and drive meaningful advances to the quality and breadth of our ML & LLM solutions to boost Qventus intelligence and outcomes. You’ll work cross-functionally with clinicians, data scientists, data platform engineers, and product experts to shape solutions that deliver measurable value.
This is a high-impact role that will influence our models and products that improve the quality of healthcare and save patient lives, strengthen customer outcomes, and shape our company’s growth trajectory.
Key Responsibilities
- Build, refine, and maintain statistical and machine learning models at scale, including NLP and explainable AI approaches.
- Develop, tune, and deploy LLM systems with an eye towards rigorous evaluation of performance and user trust.
- Design comprehensive experiments and devise impactful analytics to measure the net impact of AI in healthcare settings.
- Perform hands-on data engineering: cleaning, transforming, and building pipelines (SQL, Python).
- Collaborate with cross-functional teams to translate data insights into deployed products.
- Strong ability to communicate findings clearly to both technical and non-technical audiences.
What We’re Looking For
- 3+ years of relative industry experience developing, launching, and maintaining ML systems in production at scale.
- 1+ years experience developing and tuning LLM systems.
- Strong cross-functional communication with technical and non-technical partners.
- Hands-on data engineering experience manipulating data sets, data cleaning, and pipelines with strong coding skills in Python and SQL.
- Strong software development foundations - dedication to high code quality, stable architecture
Bonus Points For
- Background in healthcare data or hospital operations.
- Experience with Databricks, AWS, DBT, or similar platforms.
- Master’s degree or equivalent professional experience in Computer Science, Statistics, or related fields
Compensation for this role is based on market data and takes into account a variety of factors, including location, skills, qualifications, and prior relevant experience. Salary is just one part of the total rewards package at Qventus. We also offer a range of benefits and perks, including Open Paid Time Off, paid parental leave, professional development, wellness and technology stipends, a generous employee referral bonus, and employee stock option awards.
Salary Range $130,000—$170,000 USDQventus values diversity in its workforce and proudly upholds the principles of Equal Opportunity Employment . We welcome all qualified applicants and ensure fair consideration for employment without discrimination based on any legally protected characteristics, including, but not limited to: veteran status, uniformed service member status, race, color, religion, sex, sexual orientation, gender identity, age, pregnancy (including childbirth, lactation and related medical conditions), national origin or ancestry, citizenship or immigration status, physical or mental disability, genetic information (including testing and characteristics) or any other category protected by federal, state or local law (collectively, "protected characteristics"). Our commitment to equal opportunity employment applies to all persons involved in our operations and prohibits unlawful discrimination by any employee, including supervisors and co-workers.
Qventus participates in the E-Verify program as required by law and is committed to providing reasonable accommodations to individuals with disabilities in compliance with Americans with Disabilities Act (ADA). In compliance with the California Consumer Privacy Act (CCPA), Qventus provides transparency into how applicant data is processed during the application process. Candidate information will be treated in accordance with our candidate privacy notice.
*Benefits and perks are subject to plan documents and may change at the company's discretion.
*Employment is contingent upon the satisfactory completion of our pre-employment background investigation and drug test.
Originally posted on Himalayas