Data Scientist, Strategic Intelligence

Accountabilities

• Develop, validate, deploy, and maintain predictive models for customer propensity, churn, account health, forecasting, segmentation, and portfolio risk.

• Apply natural language processing (NLP), large language models (LLMs), and generative AI to extract meaningful signals from unstructured data, including news, filings, CRM notes, customer interactions, surveys, and support content.

• Transform qualitative information into structured, governed features that support reliable analytics and machine learning applications.

• Combine internal datasets with external information sources to enrich customer, account, product, and market intelligence.

• Evaluate and onboard external data sources by assessing data quality, coverage, licensing, and suitability for analytical use cases.

• Identify and quantify commercial growth opportunities, including cross-selling, upselling, and untapped market potential, using transparent assumptions and sensitivity analysis.

• Build AI-enabled workflows for summarization, entity extraction, signal detection, and decision support, incorporating appropriate human oversight and controls.

• Prototype analytical solutions, design experiments, define success metrics, and measure the business value delivered by models and AI initiatives.

• Operationalize models across Databricks and Azure environments, applying version control, testing, deployment, performance monitoring, and model-drift detection.

• Collaborate with analytical engineers and commercial data engineering teams to develop feature pipelines and curated datasets.

• Deliver analytical insights through dashboards, Power BI, Tableau, and web-hosted business intelligence applications.

• Document data sources, model features, assumptions, lineage, validation results, and limitations to support transparency and reproducibility.

• Partner with commercial strategy, sales, customer success, finance, product, and technology stakeholders to translate business challenges into analytical solutions and actionable recommendations.

• Apply robust model governance practices, including back-testing, explainability, bias checks, and validation, while ensuring data quality and compliance with approved information security policies.

• Monitor emerging AI and machine learning techniques, recommend high-value applications, and contribute to data science standards and best practices across the team.

Requirements

• At least five years of experience in data science, applied machine learning, quantitative modeling, or a closely related field.

• Strong Python skills, including experience with libraries such as pandas, scikit-learn, statsmodels, and relevant NLP or deep learning frameworks, alongside proficiency in SQL.

• Hands-on experience building predictive models, such as classification, regression, survival analysis, and time-series models, covering feature engineering, validation, and monitoring.

• Practical experience with NLP, unstructured text analytics, and generative AI techniques, including embeddings, retrieval, prompt design, and model evaluation.

• Strong foundations in statistics, econometrics, causal inference, or experimental design, with the ability to interpret uncertainty and evaluate model limitations.

• Experience with Databricks, Spark, MLflow, or comparable cloud-based data and machine learning platforms; Azure experience is preferred.

• Familiarity with source control, automated testing, deployment practices, and the operational requirements of production machine learning models.

• Experience delivering analytical outputs through business intelligence tools such as Power BI or Tableau.

• Ability to translate complex business questions into analytical approaches, quantify commercial impact, and communicate findings clearly to technical and non-technical audiences.

• Experience working with commercial, customer, financial, risk, product, or operational data.

• Strong problem-solving, critical-thinking, stakeholder-management, and communication skills, with sound judgment regarding model reliability, limitations, and uncertainty.

• Demonstrated ability to use AI tools to support innovation and operational efficiency, with an understanding of AI risk management, ethical governance, and responsible adoption.

• A bachelor's degree in economics, statistics, mathematics, data science, computer science, engineering, or another quantitative discipline, or equivalent practical experience. A master's degree or PhD is preferred.

• Preferred qualifications include an economics or econometrics background; experience in credit risk, financial services, risk analytics, SaaS, or enterprise software; and customer analytics expertise in propensity, churn, account health, or lifetime value modeling.

• Additional preferred experience includes external data integration, entity resolution, Unity Catalog, Delta Lake, Azure ML, Azure OpenAI, and the development and deployment of AI-enabled applications.

• Relevant certifications in Databricks, cloud platforms, or machine learning are advantageous but not required.

Benefits

• Salary: Anticipated US base salary range of $116,500–$169,000 per year, depending on experience, education, skills, and location.

• Incentive compensation: Eligibility for additional incentive compensation alongside base salary.

• Healthcare: Medical, dental, and vision insurance.

• Retirement savings: Access to a 401(k) plan with opportunities for employee and company contributions.

• Time off and family support: Paid time off and parental leave.

• Insurance protection: Life, disability, and accident insurance.

• Employee savings: Discounted employee stock purchase plan.

• Professional development: Tuition reimbursement to support continued learning and career growth.

• Inclusive workplace: Commitment to equal employment opportunities and reasonable accommodations in accordance with applicable laws.