Develop automated analytics products and conduct sophisticated data analysis to address critical business and client questions
Design, implement, and deploy advanced data science solutions across multiple domains
Guide data scientists and ensure high-quality, scalable analytic solutions
Lead the design and implementation of advanced machine learning, generative AI, statistical modeling, and unsupervised learning approaches across structured and unstructured data
Build and deploy scalable, automated data science products and reusable analytic assets across multiple projects and domains
Analyze large-scale, high-dimensional datasets using Python, R, SQL, and distributed computing platforms to identify trends and actionable insights
Translate complex analytical outputs into clear dashboards, reports, and presentations for technical and non-technical audiences
Make key analytic and architectural design decisions, define project goals based on strategic business needs, and own final solution outcomes
Lead code development and reviews, establish best practices, and mentor data scientists in advanced modeling and responsible AI usage
Evaluate and adopt emerging tools, advanced frameworks, and generative AI technologies to continuously enhance analytical capabilities and workflows
Collaborate with cross-functional teams to integrate advanced analytics and AI solutions into products, workflows, and business processes

Requirements

Relevant degree preferred
Advanced degree in applied mathematics, statistics, computer science, econometrics, or a related field is a plus
7 or more years of relevant experience required
Proven ability to apply advanced modeling, forecasting, anomaly detection, and natural language processing required
Demonstrated advanced knowledge of data science methodologies, statistical modeling, machine learning, and generative AI techniques required
Strong understanding of Natural Language Processing (NLP) techniques, including text classification, sentiment analysis, information extraction, and Large Language Model (LLM)-based approaches required
Expertise in programming languages such as Python, R, SAS, SQL, or similar tools required
Knowledge of responsible AI principles, including model explainability, bias detection, fairness assessment, and ethical use of data
Experience deploying, monitoring, and maintaining machine learning and generative AI models in production environments
Experience implementing model governance practices such as documentation standards, version control, validation protocols, and audit readiness
Experience working with cloud-based and distributed data platforms and modern analytics frameworks such as Databricks
Strong analytical, problem-solving, communication, and presentation skills with the ability to convey complex concepts to non-technical audiences
Experience guiding or mentoring data scientists.

Benefits

Comprehensive benefits plan
Incentive eligible

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

data analysisdata science solutionsmachine learninggenerative AIstatistical modelingunsupervised learningnatural language processingforecastinganomaly detectionmodel governance

Soft skills

analytical skillsproblem-solvingcommunication skillspresentation skillsmentoringleadershipcollaboration

Certifications

advanced degree in applied mathematicsadvanced degree in statisticsadvanced degree in computer scienceadvanced degree in econometrics