Senior Data Scientist

How you will make an impact:

Leverage the best of modern deep learning & large language models with traditional data science techniques to create powerful hybrid models with real uplift.
Brainstorm, prototype, prove, deploy, and realize the value of your work in market quickly.
Everything you would expect on a world-class data science team solving world-class problems. Big data. Federated learning. Unstructured data challenges. Timeseries and sequence modelling. A self-serve buffet of techniques from GLMs to XGBoost to Transformers.
Tell stories with your data. Inspire trust in customers, stakeholders, and prospects by turning murky math into a powerful message that drives the bottom line.
Who you are and why we want to work with you:

You like getting things over the line. You have an insatiable desire to deliver value now and improve next. MVP perfection is achieved not when there is nothing more to add, but when there is nothing left to take away.
You are not a software engineer, but you give them a run for their money. You prefer Python to R and don’t understand why there is still a debate. Jupyter is a necessary evil, and you’ve never met a command line that scared you away.
You still do a better job than Claude, and you’re skeptical of your friends who say they never code any more.
You love to take initiative and spearhead new projects, even if they are not well defined.
You build systems bigger than you. You contribute to open source, build packages your peers want to use, or design frameworks to elevate your team. Reuse is a strategy, not a buzzword.
Skills needed to succeed:

Bachelor's degree in Computer Science, Data Science, Biostatistics, Mathematics, or similar related field AND 3-5 years of professional data science experience building predictive models
OR Master’s or Ph.D in Computer Science, Data Science, Biostatistics, Mathematics, or similar related field AND 1-2 years of professional data science experience building predictive models
Hands-on comprehensive experience with deep learning frameworks such as PyTorch or TensorFlow, and an understanding of modern AI architectures (e.g., Transformers, Neural Networks, LLMs). 
Advanced proficiency in Python and its core data science ecosystem (e.g., Pandas, NumPy, Scikit-learn, LightGBM, etc.) 
Strong technical communication and collaboration skills within agile squads and DS leadership
Familiarity with SQL, Relational databases, and cloud environments like AWS/Azure/Google/Databricks
MLOps and hands-on experience taking models from notebook to production — Experiment tracking, packaging, containerization (Docker), model serving and APIs, CI/CD, and pipeline orchestration
Bonus Qualifications:

Fluency with actuarial methods and working with actuaries is a plus 
Familiarity with healthcare and medical data
Familiarity with underwriting and claims, or predicting long-tailed and/or rare events
What We Offer:

A fun, team-oriented startup culture.
Generous stock options - we all get to own a piece of what we’re building.
Unlimited vacation days.
Flexible schedule that supports working from home.
Full benefits package includes medical, dental, vision, 401k, paid paternal leave, and more.
Ample opportunities to learn and take on new responsibilities.
We are an equal opportunity employer.

Salary Range: $140,000-170,000k base salary annually.

Skills Required
Bachelor's in Computer Science/Data Science/Biostatistics/Mathematics and 3-5 years professional data science experience building predictive models OR Master's/PhD and 1-2 years professional data science experience
Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow
Understanding of modern AI architectures (Transformers, Neural Networks, LLMs)
Advanced proficiency in Python and core data science libraries (Pandas, NumPy, scikit-learn, LightGBM)
Strong technical communication and collaboration skills within agile squads and with DS leadership
Familiarity with SQL, relational databases, and cloud environments (AWS/Azure/Google/Databricks)
MLOps experience taking models from notebook to production: experiment tracking, packaging, containerization (Docker), model serving/APIs, CI/CD, pipeline orchestration
Fluency with actuarial methods and working with actuaries
Familiarity with healthcare and medical data
Familiarity with underwriting and claims or predicting long-tailed/rare events