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At Solace, data isn't just about understanding the past; it's about predicting the future. We are a rapidly scaling startup where we work hard, move fast, and wear many hats. The Solace Data Team is looking for a Data Scientist who excels at forecasting, prediction, and machine learning to help us anticipate patient needs and optimize our operations. Reporting directly to the VP of Data, you will have the opportunity to lead high-impact projects that directly influence our strategic decision-making. In this role, you will go beyond descriptive analytics to build the predictive engines that drive our business. Whether you are forecasting operational volume, modeling patient risk, or designing rigorous A/B tests, you will bridge the gap between advanced statistics and practical business application, ensuring our models are robust, interpretable, and impactful.
Job Responsibility:
Build Predictive Models: develop and refine machine learning models to solve critical business problems
Drive Forecasting Rigor: own the development of time-series forecasts that guide capacity planning and resource allocation
Design & Analyze Experiments: lead the design and analysis of A/B tests and causal inference studies
Uncover Deep Insights: apply advanced statistical methods to complex datasets to find patterns
Productionize Solutions: work closely with Data Engineers to take models from a local notebook to a production environment
Communicate Complexity: translate complex statistical findings into clear, actionable narratives for non-technical stakeholders
Requirements:
Statistical Expertise: strong background in statistics, probability, and mathematics (e.g., hypothesis testing, regression analysis, time-series forecasting)
Python & ML Proficiency: fluent in Python and its data science ecosystem (pandas, scikit-learn, statsmodels, NumPy)
Forecasting Experience: hands-on experience with time-series analysis and forecasting techniques (e.g., ARIMA, Prophet, exponential smoothing)
Advanced SQL: can write complex queries to wrangle data from Snowflake
Preferred Masters Degree or PHD: Data Science, Applied Science, or related fields
Data Privacy Awareness: understand the importance of protecting patient data, familiar with best practices for handling PHI and PII
Startup DNA: self-starter comfortable with ambiguity, takes ownership of problems, willing to wear many hats
Applicants must be based in the United States
Nice to have:
Deployment Experience: experience using tools like Docker, Airflow, or MLflow to deploy and monitor models in production
Healthcare Experience: experience working with healthcare or insurance claims data
NLP & Unstructured Data: experience applying Natural Language Processing (NLP) techniques to extract insights from unstructured text data
Marketplace Matching: experience designing matching algorithms or ranking systems for two-sided marketplaces
dbt & Engineering Skills: comfort reading or writing dbt models to understand the lineage of data