Data Scientist - Extensions

Role Description

In this role, you'll research, develop, and productize data science capabilities that enhance and expand our product performance on real enterprise use cases - working across a wide range of prediction tasks, data types, and business domains. You'll go deep on hard data science problems, collaborate closely with R&D on product capabilities, and ship production-grade work that has a direct impact on Production use cases.

Key Responsibilities

• Research and develop data science methods that improve NEXUS predictive performance across diverse enterprise datasets, industries, and prediction task types.
• Design and implement robust, production-quality Python components with a strong focus on correctness, generality, and reusability.
• Deeply understand the characteristics of real-world enterprise data and develop strategies that help NEXUS handle them reliably.
• Run rigorous experiments to measure the impact of new approaches, design meaningful benchmarks, and use results to guide prioritization.
• Work across a wide variety of structured data problems - including but not limited to classification, regression, ranking, and forecasting.
• Collaborate closely with the Engineering and Research teams to develop a deep understanding of NEXUS model behavior and use that knowledge to inform your work.
• Work with Applied AI Engineers to validate approaches on real customer datasets and translate findings into product capabilities.
• Contribute to technical documentation and internal best practices, helping the broader team apply new capabilities correctly and confidently.

Qualifications

• 5+ years of experience in data science or machine learning roles.
• Strong Python skills, including fluency with pandas, numpy, and scikit-learn.
• Deep hands-on experience with traditional ML models: XGBoost, LightGBM, CatBoost, and similar gradient boosting frameworks.
• Solid understanding of what makes real-world tabular data challenging: class imbalance, high cardinality, distribution shift, missing values, and more.
• Strong experimental mindset - comfortable designing benchmarks and drawing rigorous conclusions from noisy results.
• Ability to work autonomously and drive work from idea to shipped output.

Requirements

• Familiarity with tabular foundation models (TabPFN, CARTE, or similar).
• Competitive data science experience (Kaggle, DrivenData, or similar) - especially top finishes on tabular competitions.
• Background in a domain where structured prediction matters: finance, supply chain, healthcare, retail, or industrial.
• Experience contributing to or designing internal ML libraries or shared tooling.
• Familiarity with DuckDB, Polars, or modern in-process analytics engines.
• Comfort reading ML research papers and translating findings into practical implementations.

Benefits

• Competitive compensation with salary and equity.
• Comprehensive health coverage for you and your dependents.
• Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys.
• Relocation support for employees moving to join the team in one of our office locations.
• A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action.