Data Scientist

Prove

1d•$125,000 - $170,000•Remote

About The Position

We are seeking a Senior Data Scientist who combines strong expertise in statistical analysis and applied machine learning with practical experience in production-oriented workflows. This role is not just about building models in isolation—you will partner closely with Senior Machine Learning Engineers to ensure models are production-ready, monitored, and continuously improved. You’ll focus on generating insights, developing models, and proactively collaborating in monitoring deployed algorithms. Beyond the technical, you’ll help identify opportunities for model-driven enhancements and business and customer recommendations based on real-world performance.

Requirements

5+ years of experience applying machine learning and statistics to business problems.
Master’s or PhD in Statistics, Computer Science, Data Science, or related field (or equivalent experience).
Prior exposure to production ML environments and workflows.
Strong proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow).
Strong background in statistical methods (supervised/unsupervised learning, classification models, etc.), experimental design, and data visualization (Looker) and storytelling.
Strong proficiency in SQL (Snowflake) with an ability to work with raw data and collaborate with data engineering teams to optimize data pipelines.
Proficiency with cloud platforms (AWS).
Solid understanding of APIs, model deployment processes, and monitoring practices.
Excellent communication skills with ability to bridge technical and business contexts.
Strong problem-solving and proactive ownership mindset.
Comfort working in cross-functional teams with engineers, product managers, and business leaders.
Ability to balance quick iterations with building long-term scalable solutions.

Nice To Haves

Familiarity with other programming languages such as R, Java, and Go.

Responsibilities

Develop, validate, and tune statistical and machine learning models that solve complex business problems.
Partner with engineers to ensure models are designed with production deployment in mind.
Design experiments and evaluate models using robust statistical methodologies.
Build and maintain dashboards that proactively monitor product efficacy and distribute key insights across product and customer domains.
Collaborate with ML engineers on deployment pipelines, APIs, and infrastructure.
Proactively monitor deployed models for drift, accuracy, and reliability.
Provide insights and business recommendations based on model performance in production.
Recommend retraining or refinement strategies in response to performance changes.
Translate model results into actionable recommendations for both product and business teams.
Identify opportunities for model improvements that drive up-sell, revenue growth, and cost reduction.
Communicate results clearly to both technical and non-technical stakeholders.
Work side-by-side with Senior Machine Learning Engineers to ensure smooth handoff from research to deployment.
Mentor team in best practices for applied ML and production readiness.
Contribute to evolving data science standards and playbooks that prioritize operational impact.

Benefits

Competitive compensation and benefits.
A high-impact environment and data-driven culture where your models make it into production and drive measurable business outcomes.
Opportunities to shape both data science practices and production ML systems.
Competitive salaries & Bonus Plan (for eligible roles) and Equity Plan
Modern Health for financial, mental, and physical wellness
401(k) Retirement Plan & Match (US Offices)
Unlimited Vacation and Flexible hours
Comprehensive medical benefits for you and your family
Emotional & Physical Wellness – Access to wellness services (EAP & Prove Well-Being Reimbursement)
Bottomless snacks & beverages for certain office locations
Daily GrubHub stipend for lunch if coming into the office (US Offices)