Job Summary
The Machine Learning Specialist designs and develops analytical data products, machine learning models, and AI-enabled services that support evidence-based policy and improved public services. Working with cross-functional teams, external stakeholders, data engineers, analysts, and business leaders, this role applies statistical analysis, data science, artificial intelligence, data modeling, de-identification, and synthetic data techniques to complex challenges. The position provides hands-on technical leadership and strategic advice throughout the data product lifecycle while ensuring that ML solutions are reliable, explainable, privacy-compliant, ethical, and aligned with data strategy.

Key Responsibilities
• Advise stakeholders and project teams on when and how to apply machine learning, including identifying appropriate use cases, data prerequisites, risks, limitations, and expected business value.
• Analyze, organize, normalize, clean, and integrate raw data from multiple sources to prepare it for descriptive, predictive, and prescriptive modeling.
• Design, develop, train, validate, document, and refine machine learning and statistical models using appropriate algorithms, packages, tools, and programming languages.
• Conduct ML-driven analysis of large datasets and communicate findings through analytical models, reports, visualizations, dashboards, and actionable insights for services and policymaking.
• Integrate trained machine learning models and analytical capabilities into applications, data products, and full-stack analytics or AI solutions as required.
• Develop and maintain auditing, accountability, transparency, metadata, data quality, privacy, security, and ethical governance mechanisms for ML products and services.
• Provide technical leadership, coaching, mentoring, requirements analysis, stakeholder engagement, risk escalation, and delivery support within multi-disciplinary and multi-vendor project environments.

Required Qualifications
• Minimum 9 years of relevant experience.
• Minimum 6 years of experience using statistical and programming languages such as Python, R, and SQL for data analysis, data science, and machine learning.
• Minimum 6 years of experience building analytical and quantitative analysis models to support business, operational, policy, or service-delivery objectives.
• Minimum 6 years of experience preparing and transforming data for prescriptive and predictive modeling, including data cleaning, feature preparation, normalization, and quality assessment.
• Minimum 6 years of experience applying data analytics and data science methods, including complex statistical modeling and interpretation of analytical results.
• Minimum 6 years of experience applying artificial intelligence or machine learning to data science and analytics use cases, such as anomaly detection, predictive monitoring, classification, forecasting, data transformation, and automation.
• Demonstrated knowledge of statistical classification and machine learning techniques, including k-means clustering, hierarchical clustering, partition trees, logistic regression, and related supervised and unsupervised methods.
• Demonstrated experience gathering and documenting client requirements, framing analytical questions, developing analytical products, capturing technical and business metadata, and communicating complex findings to technical and non-technical audiences.

Preferred Qualifications
• Minimum 5 years of experience with data sharing, data linkage, de-identification, metadata, data quality, data ethics, synthetic data, data literacy, and the responsible use of data.
• Minimum 5 years of experience combining and analyzing raw data from diverse sources across multiple business or subject-matter domains.
• Experience preparing visualizations, dashboards, and analytical models; applying statistical and data-mining techniques to business issues; and working with large datasets, including a minimum of 6 years of statistical and data-mining experience and 4 years of experience with large datasets.