About the Position

This role will be responsible for applying advanced analytics, statistical modeling, machine learning, and artificial intelligence techniques to complex organizational data to generate actionable insights and support data-driven decision-making. The Data Scientist will collaborate with customer to identify high-value analytical opportunities, develop predictive and descriptive models, and translate complex data findings into understandable recommendations.

The role will work within the cutomers modern cloud-based data ecosystem and leverage platforms and technologies such as Databricks, Azure, Python, R, SQL, Power BI,  MLflow, and Generative AI while adhering to enterprise standards for data governance, security, privacy, explainability, and responsible AI.

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What you bring

• Strong knowledge of statistics, machine learning algorithms, experimental design, predictive modeling, and analytical methodology.
• Deep hands-on expertise with Python and SQL, with experience using R considered beneficial.
• Experience with Python data science and machine learning libraries such as Pandas, NumPy, scikit-learn, PySpark, TensorFlow, or PyTorch.
• Hands-on experience with Databricks, including notebooks, Delta Lake, MLflow, Unity Catalog, and PySpark.
• Experience with cloud analytics and machine learning platforms such as Microsoft Azure and Azure Machine Learning.
• Experience developing and operationalizing machine learning models and familiarity with MLOps concepts.
• Experience preparing, cleansing, transforming, integrating, and analyzing structured and unstructured datasets.
• Familiarity with Generative AI, large language models, embeddings, vector search, RAG, prompt engineering, AI agents, and model evaluation techniques.
• Working knowledge of data visualization and business intelligence platforms such as Power BI.
• Understanding of data governance, privacy, security, model governance, responsible AI, and explainable AI concepts.
• Ability to clearly articulate analytical results and recommendations based on empirical evidence.
• Strong problem-solving skills and demonstrated ability to investigate complex or ambiguous business problems.
• Ability to manage multiple concurrent analytical efforts with a high degree of autonomy and independent judgment.
• Exceptional interpersonal and communication skills, including the ability to collaborate across technical and business teams.
• Ability to effectively represent internal and external stakeholders
• Experience developing AI/ML solutions within regulated, government, financial, audit, or other highly governed environments.
• Experience with Databricks Mosaic AI, MLflow, AI/BI, Genie, vector search, or related AI capabilities.
• Experience with Generative AI and agentic AI solutions.
• Familiarity with agile methods and experience working within cross-functional agile teams.
• Professional certification in data science, machine learning, Databricks, Microsoft Azure, or artificial intelligence.

Nice to have

• Experience in financial regulation, audit or federal environments.

Working with us

• You will work inside a secure, private cloud environment with highly confidential data.
• You must be authorized to work in the United States and able to pass a background check.
• We are looking for people who can start within a few weeks.

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What You'll Do

• Collaborate with customer to identify business problems and opportunities that can be addressed through advanced analytics, machine learning, artificial intelligence, and statistical analysis.
• Translate complex business questions into analytical approaches, measurable hypotheses, experiments, models, and data requirements.
• Explore, analyze, and interpret large, complex datasets to identify meaningful trends, relationships, anomalies, patterns, and opportunities.
• Develop descriptive, diagnostic, predictive, and prescriptive analytical models to support business and operational decision-making.
• Design, develop, test, validate, and deploy machine learning models using appropriate supervised and unsupervised learning techniques.
• Apply statistical techniques including regression, classification, clustering, forecasting, hypothesis testing, feature selection, and experimental design.
• Develop reusable analytical pipelines and data science solutions using Python, R, SQL, PySpark, and Databricks.
• Leverage the Databricks Lakehouse platform to perform large-scale data exploration, feature engineering, machine learning, and AI workloads.
• Use MLflow and related MLOps capabilities to track experiments, manage model versions, evaluate model performance, and support controlled production deployment.
• Develop and evaluate Generative AI solutions, including large language model applications, retrieval-augmented generation (RAG), natural language processing, document intelligence, and AI agents where appropriate.
• Establish evaluation methods and metrics to assess the quality, accuracy, reliability, explainability, fairness, and business value of analytical and AI solutions.
• Partner with Data Engineers and Data Architects to define data requirements and ensure that appropriate, high-quality, governed data is available for analytics and AI workloads.
• Identify data quality issues, biases, limitations, and gaps that may affect analytical results or model performance, and work with appropriate teams to remediate issues.
• Develop dashboards, visualizations, reports, analytical products, and data narratives that clearly communicate findings and recommendations.
• Translate highly technical analytical results into concise, actionable information for senior leadership and non-technical stakeholders.
• Develop appropriate technical documentation covering analytical methods, model assumptions, data lineage, training datasets, evaluation results, limitations, and intended use.
• Support model monitoring and periodically evaluate production models for accuracy, performance degradation, data drift, and other risks.
• Follow customer standards for data governance, information security, privacy, records management, model governance, and responsible use of artificial intelligence.
• Research emerging technologies and analytical methodologies and recommend opportunities that could enhance customer data and AI capabilities.

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