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.