Data Scientist – Pilot Machine Learning

Position Summary

CoAdvantage is adding a dedicated Data Scientist to conceive, prototype, and validate new ML models from existing internal data assets. This role sits at the front end of the model lifecycle: identifying high-value prediction problems, proposing statistical approaches, running experiments to validate feasibility, and producing handoff-ready artifacts that the Staff MLOps Engineer can carry to production.

Core Responsibilities

• Model ideation and problem framing: Identify prediction and estimation problems that are solvable from CoAdvantage's existing data assets. This includes reviewing operational data for signal, scoping the problem with business stakeholders, writing a pre-registration that specifies the target variable, success criterion, and identification strategy, and getting sign-off from the AI Experimentation Lead before investing in prototyping. The expectation is at least two novel model proposals per quarter, grounded in a data feasibility check before they enter the backlog.

• Statistical design and experimental validation: Design and run the statistical work that validates whether a candidate model is viable: exploratory analysis, feature relevance tests, baseline benchmarks, and where relevant, controlled or quasi-experimental designs. The Data Scientist is the methodological author of record for each candidate — identification strategy, assumptions, and known failure modes are documented in writing before a model proceeds to MLOps handoff. Relevant methods include supervised learning baselines, time-series decomposition, causal inference (difference-in-differences, propensity matching, synthetic control, interrupted time-series), and power analysis for experiment sizing. The role is expected to span methods rather than optimize a single toolkit.

• Prototype development and handoff packaging: Build prototype model code to the standard required for MLOps handoff: versioned repository, documented training pipeline, reproducible validation results, model card, and a handoff brief that specifies the serving contract, retrain frequency, monitoring schema, and known data dependencies. The MLOps Engineer should be able to carry the handoff to production without a significant rediscovery phase. Prototype code is expected to be production-oriented even at the prototype stage — not notebook-only. The Data Scientist is responsible for the code quality up to handoff; the MLOps Engineer owns it from handoff forward.

• Collaboration with the data team on feasibility: Before committing a candidate model to the backlog, validate data feasibility with the data team: source system availability, refresh cadence, data quality, tenant isolation, and governance constraints. Push back on infeasible proposals early rather than after significant prototype investment.

• Documentation and methodological transparency: Every model that reaches the handoff stage carries a complete model card: problem statement, training data window, feature definitions, identification strategy, baseline benchmarks, known failure modes, and the reconciliation plan. The bar is reproducibility from underlying data.

Required Qualifications

• Three or more years of experience as a Data Scientist building and validating ML models, including at least one model that reached a production or near-production state.

• Strong Python and SQL. Comfortable authoring exploratory analysis, feature engineering, and model training code without an engineering intermediary.

• Working fluency with core statistics: probability distributions, hypothesis testing, experimental design, power analysis, and model calibration. Causal inference fluency (difference-in-differences, propensity matching, synthetic control) is required — not optional.

• Demonstrated breadth across ML problem types: at least two of supervised classification, regression, time-series forecasting, anomaly detection, or propensity modeling.

• Hands-on experience with AI-assisted coding tools (Claude Code, Copilot, Cursor, or equivalent) as a daily driver, with code commits or repositories to demonstrate the practice.

• Able to produce clear written documentation: model cards, pre-registrations, and handoff briefs that a technical peer can act on without follow-up.

• Ability to propose novel problem framings from raw data rather than only executing predefined specifications.

Preferred Qualifications

• Prior experience in a PEO, HR outsourcing, insurance brokerage, BPO, or other labor-intensive services organization.

• Exposure to pricing, underwriting, churn, contact volume, or workforce planning use cases.

• Familiarity with cloud ML platforms (Azure ML, Databricks, Vertex AI) and feature store concepts.

• Experience working under data governance constraints typical of regulated multi-tenant environments (HIPAA, PII, tenant isolation).

• Demonstrated track record of self-directed model ideation — examples of models you proposed, not only models you were assigned.