Note: The job is a remote job and is open to candidates in USA. Solace is a Series C startup focused on redefining healthcare by providing expert advocates to help patients navigate the complex healthcare system. The Data Scientist Manager will lead and grow the Data Science team, managing predictive analytics and production machine learning systems to drive business impact.
Responsibilities
- Coach and Develop the Team: Manage and mentor data scientists, giving them the feedback and growth opportunities to build stronger judgment and take on more ownership
- Own the Roadmap: Set and drive execution of the Data Science roadmap, acting as the intake point for cross-functional requests and making deliberate prioritization decisions
- Build Cross-Functional Relationships: Partner closely with leaders to ensure that data science is embedded in how decisions get made, not layered on after the fact
- Drive Marketplace Forecasting: Own the forecasts and targets that keep advocate capacity, funnel volume, and supply and demand aligned, giving the business clear numbers to plan and hire against
- Own Production ML Systems: Own the machine learning models that run inside our core product, including matching and quality scoring, from prototype through production
- Establish Best Practices: Set the standard for how data science gets done at Solace, including code review, experimentation design, model validation, and monitoring
Skills
- 6+ Years of Data Science Experience: Proven track record building and shipping predictive models and machine learning systems, ideally at a product-led tech startup or marketplace
- 2+ Years of Management or Leadership Experience: Direct experience managing, mentoring, or leading data scientists or a similar technical team
- Deep Python, ML & SQL Fluency: Fluent in Python, its data science ecosystem, and the advanced SQL needed to pull your own data from Snowflake. You can read, debug, and meaningfully critique your team's code and models
- Production ML Experience: You have taken models from notebook to production, with experience monitoring, detecting drift, and iterating on models running live
- Statistical & Forecasting Expertise: Strong background in statistics and time-series forecasting (e.g., ARIMA, Prophet)
- Experimentation Rigor: Strong grounding in A/B testing and causal inference, with a track record of designing experiments that produce trustworthy results
- Exceptional Communication: You can translate complex modeling work into clear narratives for non-technical executives and cross-functional partners
- Startup DNA: You thrive in ambiguity, move with urgency, and are comfortable wearing multiple hats
- Marketplace Matching Experience: Experience designing matching algorithms or ranking systems for multi-sided marketplaces
- Healthcare Experience: Experience working with healthcare or insurance claims data
- MLOps Tooling: Experience packaging, deploying, and monitoring models in production, including containerization (Docker), experiment tracking and model registries (e.g. MLflow, SageMaker), and CI/CD for model deployment
- Dbt & Engineering Skills: Comfort reading or writing dbt models to understand data lineage
- Advanced Degree: Master's or PhD in Data Science, Statistics, Applied Math, or a related field
Company Overview