Data Scientist

The Data Scientist will turn complex product, customer, and operational data into models and insights that guide high-impact decisions. The role spans exploratory analysis, experimentation, predictive modeling, and production deployment across structured and unstructured data.

The team is building reliable data products used by customers and internal operators, with a strong emphasis on measurable business outcomes, statistical rigor, and maintainable engineering. This role partners closely with ML engineers, data engineers, product managers, and business stakeholders from problem definition through deployment and monitoring.

Key Responsibilities

• Develop and productionize predictive models for forecasting, classification, ranking, recommendation, and anomaly detection using Python, SQL, and scikit-learn, XGBoost, or PyTorch
• Design and analyze A/B tests and quasi-experimental studies, defining success metrics, power requirements, guardrails, and clear recommendations for product decisions
• Build reusable data pipelines and analytical datasets with SQL, dbt, and Spark across cloud data platforms such as Snowflake, BigQuery, or Databricks
• Translate ambiguous business and product questions into well-scoped analytical approaches, model objectives, and measurable deliverables
• Evaluate model performance using appropriate statistical and ML metrics; investigate bias, calibration, data leakage, drift, and failure modes before release
• Deploy and monitor models and analytical services in partnership with ML engineering and MLOps teams, using tools such as Docker, Kubernetes, MLflow, or cloud-native ML platforms

• 3–7 years of experience in data science, applied statistics, machine learning, or a closely related role, including experience delivering models or analytical products used in production
• Advanced Python and SQL skills, with practical experience using pandas, NumPy, scikit-learn, and notebook-based development workflows
• Strong foundation in statistical inference, experiment design, regression, classification, model validation, and communicating uncertainty
• Experience working with large-scale data using Spark, Databricks, Snowflake, BigQuery, or comparable distributed data platforms
• Demonstrated ability to communicate technical findings clearly through data visualizations, written analysis, and presentations to both technical and non-technical audiences
• Bachelor’s or master’s degree in statistics, computer science, mathematics, economics, engineering, or a related quantitative field; equivalent practical experience is acceptable
• Bonus: Experience with causal inference, time-series forecasting, NLP or LLM applications, MLOps practices, cloud platforms such as AWS/GCP/Azure, and BI tools such as Tableau or Looker