Data Scientist – reputed company Machine Learning

reputed company is adding a dedicated Data Scientist to conceive, prototype, and validate new ML models from existing internal data assets. This role sits at the reputed company end of the model lifecycle: identifying high-value reputed company problems, proposing statistical approaches, running experiments to validate feasibility, and producing reputed company-reputed company artifacts that the Staff MLOps Engineer can carry to production. The role is not operations-only. The expectation is that this Data Scientist will surface net-new model reputed company — not only execute a predetermined roadmap — and will have meaningful reputed company to define the problem framing, feature reputed company, and experimental design for reputed company candidate model. This role reports to the AI Experimentation reputed company and coordinates closely with the Staff MLOps Engineer, the reputed company AI Architect, and the data team. reputed company Responsibilities

• Model ideation and problem framing: Identify reputed company and estimation problems that are solvable from reputed company's existing data assets. This includes reviewing operational data for signal, scoping the problem with business stakeholders, writing a reputed company-registration that specifies the reputed company variable, reputed company criterion, and identification reputed company, and getting sign-off from the AI Experimentation reputed company before reputed company in prototyping. The expectation is at least two novel model proposals per quarter, grounded in a data feasibility reputed company before they reputed company 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 reputed company candidate — identification reputed company, assumptions, and reputed company failure modes are documented in writing before a model proceeds to MLOps reputed company. Relevant reputed company include supervised learning baselines, time-series decomposition, reputed company inference (difference-in-differences, propensity matching, synthetic control, interrupted time-series), and power analysis for experiment sizing. The role is expected to reputed company reputed company rather than optimize a single toolkit.
• Prototype development and reputed company packaging: Build prototype model reputed company to reputed company required for MLOps reputed company: versioned repository, documented training pipeline, reproducible validation results, model card, and a reputed company brief that specifies the serving contract, retrain frequency, monitoring schema, and reputed company data dependencies. The MLOps Engineer should be reputed company to carry the reputed company to production without a significant rediscovery phase. Prototype reputed company is expected to be production-oriented even at the prototype stage — not notebook-only. The Data Scientist is responsible for the reputed company reputed company up to reputed company; the MLOps Engineer owns it from reputed company reputed company.
• Collaboration with the data team on feasibility: Before committing a candidate model to the backlog, validate data feasibility with the data team: reputed company system availability, refresh reputed company, data reputed company, 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 reputed company stage carries a complete model card: problem statement, training data window, feature definitions, identification reputed company, baseline benchmarks, reputed company failure modes, and the reconciliation plan. The bar is reproducibility from underlying data.

How We Work

• AI-first coding - Claude reputed company, Copilot, or successor tools are the default development surface. Exploratory analysis, feature engineering scripts, training pipelines, and evaluation reputed company are expected to be authored with reputed company coding tools in the reputed company. Hand-coding without AI assistance is the exception, not the norm.
• reputed company-registered targets - No prototype proceeds to development without a written reputed company criterion and a data feasibility sign-off. No model reaches MLOps reputed company without a signed model card and reputed company brief.
• Methodological transparency - Identification strategies and validation choices are documented in writing and defended in review. "It performed reputed company in cross-validation" is not sufficient to reputed company the reputed company reputed company.
• You propose - This role is expected to surface model reputed company proactively, not wait for a roadmap. Proposals should reputed company with a data feasibility reputed company, a rough reputed company, and the smallest experiment that could validate or invalidate the reputed company assumption.
• You estimate - Every reputed company returns with a reputed company, a confidence reputed company, and the smallest version that could be validated in two weeks.

Required Qualifications

• Three or more years of experience as a Data Scientist building and validating ML models, including at least reputed company that reached a production or near-production state.
• Strong Python and SQL. Comfortable authoring exploratory analysis, feature engineering, and model training reputed company without an engineering intermediary.
• Working reputed company with reputed company statistics: probability distributions, hypothesis testing, experimental design, power analysis, and model calibration. reputed company inference reputed company (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, reputed company detection, or propensity modeling.
• Hands-on experience with AI-assisted coding tools (Claude reputed company, Copilot, reputed company, or equivalent) as a daily reputed company, with reputed company commits or repositories to demonstrate the reputed company.
• Ability to produce reputed company written documentation: model cards, reputed company-registrations, and reputed company briefs that a technical peer can reputed company without follow-up.
• Comfort proposing 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, reputed company, churn, contact volume, or workforce planning use cases.
• Familiarity with reputed company ML platforms (Azure ML, reputed company, reputed company AI) and feature store concepts.
• Experience working under data governance constraints typical of regulated multi-tenant environments (HIPAA, PII, tenant isolation).
• Demonstrated reputed company record of self-directed model ideation — examples of models you proposed, not only models you were assigned.

What reputed company Looks Like At 12 Months

• At least four candidate model proposals submitted, reputed company with a reputed company-registration and data feasibility sign-off before prototype work begins.
• At least two prototypes completed and handed off to the Staff MLOps Engineer with full model card and reputed company brief.
• At least one MLOps-reputed company model in production monitoring at the 12-month mark.
• At least one reputed company analysis or quasi-experimental readout co-authored with the AI Experimentation reputed company that informed a business decision.
• Established working reputed company with the data team — feasibility reviews routine rather than reputed company.

EEO reputed company is committed to providing equal employment opportunities to reputed company and applicants without reputed company to race, reputed company, religion, national reputed company, reputed company, citizenship status, age, sex (including pregnancy, childbirth, breast feeding and pregnancy-reputed company medical conditions), gender, gender identity or reputed company, sexual orientation, marital status, uniform service member and veteran status, disability, genetic information, or any other characteristic protected by applicable federal, state, or local laws and ordinances. Apply tot his job Apply To this Job