Data Scientist - Senior Consultant to Manager Ernst & Young AE

We are currently hiring for Data Scientists - (Senior Consultant to Manager level) to join our Commercial / Customer Value Creation Team.

About the Role:

We re seeking an experienced data scientists to deliver insights and act as the hands on modeling lead on client engagements. You will slice and dice data using various methods and create new visions for the future. You ll frame problems, engineer features, build and deploy models, and monitor them in production to support the customer value creation activities. You will ship models that run at scale, withstand audit, and clearly link to incremental business value. While mining, interpreting, and cleaning the data, you will be relied on to ask questions, connect the dots, and uncover hidden opportunities for realizing the data s full potential. The ideal candidate will have mathematical and statistical expertise, extensive programming experience in multiple languages along with natural curiosity and a creative mind.

Core Responsibilities:

• Problem Framing & Target Definition:

• Translate commercial goals into modeling problems (e.g., churn, propensity to buy, uplift, LTV, survival/tenure) with clear success metrics and guardrails
• Identify relevant data sources to mine for client business needs, and collect large structured and unstructured datasets and variables

• Data Analysis:

• Serve as data strategist to identify and integrate new datasets and execute analytical experiments to help solve problems across various domains and industries
• Devise and utilize algorithms and models to mine data, perform data and error analysis to improve models, clean and validate data for uniformity and accuracy
• Analyze data for trends and patterns, and interpret data with clear objectives in mind

• Model Development:

• Create reproducible pipelines and a feature registry to calculate model inputs
• Develop and evaluate churn, cross/upsell propensity, uplift/treatment effect, LTV, ranking/NBA models and select algorithms fit for data, scale, and explainability
• Use clear control/holdouts, quantify precision vs. recall, calibration, and profit curves, simulate outcomes and set practical thresholds and simple policies (eligibility, suppressions, frequency caps)

• Operationalization:

• Implement analytical models in production by collaborating with software developers and machine-learning engineers
• Implement scoring and integrate outputs with client MarTech/decisioning for SMS, email, in app, IVR, and agent assist
• Set up monitoring (performance, drift, calibration, data quality), alerting, and retrain cadence with rollback criteria

• Governance, Risk & Explainability:

• Produce model cards, document features/exclusions, and run proportionate explainability/fairness checks for regulated contexts and maintain audit ready artifacts

• Enablement & Acceleration:

• Create templates, reusable code, and quick start notebooks, /coach client analysts on model usage and monitoring
• Contribute to practice POVs/case studies as needed

Technical Expertise:

• Data platforms: Azure Synapse/Databricks, Google BigQuery, Snowflake, or Amazon Redshift
• Languages: SQL, Python, R and SAS
• BI/Reporting (any): Power BI, Tableau