Data Scientist - Extensions

• Research and develop advanced data science methods to improve predictive performance across large-scale structured enterprise datasets and diverse prediction tasks.


• Design, implement, and maintain production-quality Python components with a strong focus on correctness, scalability, and reusability.


• Analyze real-world enterprise data characteristics and design strategies to ensure robust model performance under conditions such as missing data, class imbalance, and distribution shifts.


• Design and run rigorous experiments, build meaningful benchmarks, and evaluate model improvements using statistically sound methodologies.


• Work across a broad range of structured machine learning problems including classification, regression, ranking, and forecasting.


• Collaborate closely with research and engineering teams to understand model behavior and translate insights into product improvements.


• Partner with Applied AI Engineers to validate approaches on real customer datasets and transform findings into deployable capabilities.


• Contribute to technical documentation, internal tooling, and best practices to improve reproducibility and knowledge sharing across teams.

Requirements

• 5+ years of experience in Data Science, Machine Learning, or applied ML engineering roles.


• Strong proficiency in Python, including hands-on experience with pandas, NumPy, and scikit-learn.


• Deep expertise in traditional machine learning models such as XGBoost, LightGBM, CatBoost, and other gradient boosting frameworks.


• Strong understanding of real-world tabular data challenges, including missing values, class imbalance, high cardinality features, and distribution shift.


• Proven experience designing experiments, building benchmarks, and drawing rigorous conclusions from noisy or imperfect data.


• Ability to independently drive projects from research idea to production-ready implementation.


• Strong analytical and problem-solving mindset with a focus on measurable impact and empirical validation.


• Experience working with structured prediction problems in domains such as finance, healthcare, supply chain, retail, or industrial applications is a plus.


• Familiarity with tabular foundation models (e.g., TabPFN, CARTE) is a strong advantage.


• Exposure to tools such as DuckDB, Polars, or modern in-process analytics engines is a plus.


• Experience participating in competitive data science environments (e.g., Kaggle, DrivenData) or contributing to ML libraries is beneficial.


• Ability to read, interpret, and apply machine learning research papers in practical implementations.

Benefits

• Competitive compensation package including salary and equity.


• Comprehensive health coverage for you and your dependents.


• Paid parental leave for all parents, including adoptive and surrogate families.


• Relocation support for candidates joining office locations.


• Mission-driven, low-ego culture focused on ownership, collaboration, and impact.


• Opportunity to work on foundational AI systems shaping enterprise decision-making at scale.


• High degree of autonomy in a research-driven and technically challenging environment.