AI/ML Lead Engineer

Cleartelligence

1d • $80 - $110

About The Position

We are seeking a highly skilled AI Engineering Lead with a background in ML and Full Stack Engineering to join our team and take a leading role in designing and delivering advanced machine learning and predictive analytics solutions for our clients, primarily on the Snowflake platform. As an AI/ML Lead, you will be responsible for leading the end-to-end design, development, and validation of predictive models, including survival/longevity modeling, scenario-based forecasting, and risk-driver analysis, that translate complex operational data into actionable business insight. You will collaborate with data engineering, BI/analytics, and client stakeholder teams to define modeling strategy and deliver production-ready, decision-support solutions.
This is a 5 month contract role with potential for extension or conversion to Full Time Employment

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field.
• 5+ years of hands-on experience building, validating, and deploying machine learning models in production environments; consulting or client-facing project experience strongly preferred.
• Deep proficiency in Python and core ML libraries (scikit-learn, XGBoost, LightGBM, CatBoost).
• Experience with survival/time-to-event analysis and/or time-series foundation models strongly preferred.
• Experience with cloud data platforms, particularly Snowflake (including Snowflake-native ML/Snowpark workflows); familiarity with AWS or Azure a plus.
• Strong understanding of feature engineering, model evaluation methodology (SMAPE, RMSE, correlation), and uncertainty quantification.
• Experience partnering with BI/visualization teams (e.g., Tableau) to operationalize model outputs into stakeholder-ready dashboards.
• Strong customer-facing communication and presentation skills, with the ability to translate complex modeling concepts for non-technical, executive audiences (e.g., CFO, operations leadership).
• Demonstrated ability to lead a project workstream, including scoping, timeline estimation, and mentoring junior team members.
• Ability to work across multiple client engagements and with teams across geographical regions.
• Prior experience in healthcare (Medicare specifically), insurance, or another regulated, data-intensive industry a plus.
• Working knowledge of data governance and regulatory considerations (e.g., HIPAA) a plus.
• Ability to work EST hours

Responsibilities

• Lead the end-to-end design of predictive machine learning solutions, from hypothesis-driven feature identification through model deployment and monitoring.
• Partner with Data Engineering teams to define, validate, and cleanse the feature pipeline, ensuring proper handling of anomalous data and confirming that all features are knowable at the time of prediction.
• Design a longevity approach that will combine entity-level segmentation to enable risk and scenario forecasting
• Evaluate, select, and apply appropriate algorithms and frameworks.
• Train and test model/feature combinations, calculating accuracy and fit measures (e.g., SMAPE, RMSE, correlation) to identify the best-performing approach and avoid well-fitting but poorly generalizing models.
• Derive confidence bands and best-case/worst-case ranges from model fit to communicate prediction uncertainty to business stakeholders.
• Translate model outputs into operational and financial insights, including risk forecasts by segment, key contributing factors, and recommended operational levers, for delivery through BI dashboards such as Tableau.
• Collaborate with Solution Architecture, Data Engineering, Analytics and development teams to operationalize models into scalable, production-ready pipelines and reporting tools/agentic workflows.
• Support “what-if” and scenario-planning capabilities that allow clients to model the impact of changes in population mix, referral patterns, or other operational variables.
• Communicate directly with client stakeholders, including executive, financial, and operational leadership, to gather requirements, present findings, and drive adoption of predictive insights.
• Identify and mitigate model risk, including data completeness issues, rare-event uncertainty, and confounding variables, using techniques such as ensemble modeling and rigorous back-testing.
• Establish and champion ML best practices, reusable frameworks, and thorough documentation to support long-term scalability and knowledge transfer.
• Provide input into project scoping, level-of-effort estimates, and staffing plans for machine learning engagements.
• Mentor and provide technical guidance to data scientists and ML developers supporting the engagement.

Benefits

• Equal Employment Opportunity and Affirmative Action to all employees and applicants following all local, state, and federal laws and regulations governing personnel activities.

What This Job Offers

• Career Level: Senior