Staff-Data-Scientist, Data-Scientist, Machine-Learning-Scientist Jobs

About a week before Hurricane Sandy made landfall, the two leading forecast models flatly disagreed: the American one carried the storm safely out into the Atlantic, the European one turned it hard left into New Jersey. The European model wasn't running a better simulation, it was running an ensemble. Hundreds of thousands of people were evacuated on the strength of the spread in forecast distribution, days before any single forecast could have justified moving them, and on October 29th, 2012, Sandy came ashore almost exactly where the models had put it.

At Mercury, we're building the future of financial infrastructure for startups and growing businesses. We're extending credit to those businesses, and we're doing it with proprietary access to some of the best data for understanding a borrower's business, from bank transactions to financial reporting to payroll. We're looking for a Senior/Staff Data Scientists to turn that record into a lending decision, partnering closely with Credit, Product, and Engineering.

In this role, you'll build and own the inference layer behind our small business lending program; you'll ship it into production with the monitoring and documentation that make it trustworthy; and you'll help Credit and Product decide what we should be lending in the first place.

Here Are Some Things You'll Do On The Job:

• Build and own the models behind our lending program - transaction classification, recurring inflow/outflow detection, annual revenue estimation, forecasting with seasonality, anomaly detection, and more
• Turn messy data streams - bank transactions, storefront sales, and more - into a defensible view of a business's health
• Deploy models into a production environment with observability, monitoring, and tests
• Partner with Credit on underwriting policy: what data is asked for, when follow-up information is worth the friction, and how to quantify uncertainty
• Partner with Product on the shape of the product: from principal sizes and pricing to repayment terms
• Assess where predictive modeling actually moves the underwriting program
• Set the technical bar for ML in Credit

You Should:

• Have 7+ years building ML models
• Implemented models that shaped automated decisions with money on the line
• Write production-grade Python
• Have depth beyond classification models; for example: time series and forecasting, anomaly detection, and NLP models
• Be proficient with deep learning, transformers, and LLMs
• Be strong in SQL and at home in a data warehouse
• Be able to explain a model to a credit officer, a model validator, a PM, and an engineer
• Be able to set modeling standards for your data-science colleagues
• Have a quantitative degree, or the equivalent built some other way

Nice To Have:

• Direct experience modeling on bank transaction data
• Experience in small business lending or business credit cards
• Experience with revenue-based financing, merchant cash advances, or similar products

*Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.

Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.

Originally posted on Himalayas

BASE SALARY: $175,000 to $200,000 per year, paid semi-monthly

YOUR ROLE

You'll build the models that decide which carrier each auto insurance shopper sees on our click wall. The goal is to send every user to the carrier most likely to convert them, so carriers see our traffic convert and raise their bids. You'll also own the buying models behind our media spend, which are held to ROAS.

Carrier conversion data (quotes, policies and acquisition cost) is now coming back to us, and you'll be the one who puts it to work in production. The hard part is the data. Outcomes land weeks or months after the click, many sessions are sparse, the same shoppers come back, and the market drifts. Our bids also decide which users we win, so the model shapes the data it learns from next.

This is a hands-on individual contributor role. You frame problems directly with the business, find your own data, build and validate the model, and stay with it through deployment and monitoring. An ML engineer works alongside you on the production side. Auto insurance takes most of your time to start, and scope broadens to other verticals over time.

We're remote first, and most of the company works East Coast hours.

WHAT SUCCESS LOOKS LIKE

By day 60, you run our insurance marketplace media buying on your own.

By the end of your first year:

• Click wall serving that matches each user to the right carrier
• Carrier conversion data running in production models
• Automated marketplace bidding taken from proof of concept to scale
• A clear approach for repeat users and sparse sessions
• Production models that hold up through drift and changing objectives
• Output the business trusts and uses without rework

MUST HAVE

• 5+ years of hands-on applied data science with measurable business impact
• Your models drove paid media decisions: spend, bidding or ad serving. Attribution and reporting alone don't qualify.
• You built what's on your resume and can explain it down to the loss function and architecture choice, neural network fundamentals included
• You see how today's bids shape tomorrow's training data, and you've handled late labels, sparse features or drift
• You own models end to end: you find your own data, build through deployment and monitoring, and bring a point of view
• Expert Python and SQL

NICE TO HAVES

• Real ad budget spent in live auctions
• Large-scale ranking or recommendation systems
• Explaining tradeoffs to non-technical partners in plain terms
• Designing for auction feedback loops, selection bias and exploration
• Hands-on neural network work, such as embeddings of text, creative or user behavior
• AWS for data science: ECS, S3, Athena, SageMaker
• CI/CD deployment

TOTAL COMPENSATION

Base salary is set according to market rates for the nearest major metro and varies based on Launch Potato’s Levels Framework. Your compensation package includes a base salary, profit-sharing bonus, and competitive benefits. Launch Potato is a performance-driven company, which means once you are hired, future increases will be based on company and personal performance, not annual cost of living adjustments.

Originally posted on Himalayas

Prealize Health
Staff Data Scientist (Core Platform)

About Prealize Health

The Role

Key Responsibilities

• Domain Expertise: Drive the end-to-end building and execution of our custom healthcare foundation models, translating high-level clinical use cases into concrete deep learning architectures and training objectives.
• Strategic Vision: Set the technical roadmap for patient risk prediction and health trajectory modeling, pioneering the use of transformer-based architectures in the healthcare domain.
• Methodological Excellence: Establish best practices for deep learning pipelines, including self-supervised pre-training, fine-tuning paradigms, and rigorous evaluation of longitudinal healthcare data.
• Technical Leadership: Own the full ML lifecycle—from data processing and research prototyping to production deployment—while mentoring junior data scientists in modern engineering practices.
• Cross-Functional Collaboration: Partner with clinicians to encode medical domain knowledge into model architectures and work with Engineering to productionize models with high reliability and low latency.
• Platform Innovation: Experiment with novel architectures and representation learning strategies to ensure our platform remains at the forefront of AI-driven healthcare insights.
• External Evangelism: Contribute to research initiatives and represent Prealize Health’s technical expertise in the broader machine learning and healthcare data science community.

Required Qualifications

• Education: PhD and/or MS in Computer Science, Machine Learning, Statistics, or a related quantitative field.
• Experience: 6–8+ years of experience (with 4+ years specifically building and deploying ML systems in production) with a proven track record of technical leadership.
• Deep Learning Expertise: Mastery of transformer architectures, attention mechanisms, and pre-training/fine-tuning paradigms. Hands-on experience with PyTorch or TensorFlow is mandatory.
• Programming & AI Tooling: Expert proficiency in Python and distributed computing (PySpark/Spark/SQL) for large-scale data processing.
• ProficiencyinleveragingAI-assisted coding tools(e.g., Claude Code, Cursor, Codex) to acceleratedevelopment cycles and enhance code quality.
• Software Engineering Rigor: Strong skills in software design patterns, testing frameworks, CI/CD, and code quality practices.
• Strategic Mindset: Demonstrated ability to conduct independent research and translate complex findings into production systems that solve high-ambiguity problems.
• Communication: Exceptional ability to distill complex technical strategies and research findings for executive stakeholders and cross-functional teams.

Preferred Qualifications

• Healthcare Domain: Experience with large-scale structured healthcare data (Claims, ICD/CPT codes, EHR systems like Epic/Cerner).
• Advanced MLOps: Experience with MLOps tooling such as MLflow, Weights & Biases, and cloud platforms (AWS preferred).
• Specialized Modeling: Familiarity with causal inference, longitudinal modeling, or self-supervised representation learning.

We Offer:

• Flexible work environment
• Competitive base salary plus a generous bonus and equity plan
• Paid time off including holidays
• Medical, dental, vision
• 401k
• Wellness and home office benefits, and more

Pay Transparency:

Diversity, Equity & Inclusion:

Originally posted on Himalayas