Data Scientist - ML and Advanced Analytics; AI Studio

Position: Data Scientist - ML and Advanced Analytics (AI Studio)
Our client is seeking a Data Scientist with deep expertise in machine learning, statistical modeling, and applied analytics. This is a hands-on individual contributor role with opportunities to lead project work streams and mentor junior team members. The ideal candidate has a strong track record of translating complex analytical research into scalable, production-ready solutions that drive measurable business impact.
In this role, you will design, build, and deploy machine learning models and intelligent automation solutions across high-visibility initiatives. You will collaborate closely with cross-functional partners-including product, engineering, and business leaders-to identify opportunities, shape solutions, and deliver data-driven outcomes across enterprise platforms.

Key Responsibilities

• Lead end-to-end analytics and machine learning initiatives, including scoping use cases, guiding junior data scientists, and delivering production-ready solutions.
• Perform exploratory data analysis, data preparation, feature engineering, modeling, validation, and presentation of insights and recommendations.
• Design and implement predictive and descriptive models using advanced statistical, machine learning, and AI techniques to address business objectives.
• Apply a range of ML approaches such as regression, classification, clustering, tree-based models, ensemble methods, and neural networks, understanding their real-world trade-offs.
• Conduct data wrangling, matching, and ETL across diverse data sources, ensuring data quality and readiness for modeling and analysis.
• Identify source systems, perform data quality assessments, and implement validation checks during both development and production phases.
• Package, deploy, and operationalize models in collaboration with data engineering and MLOps teams, ensuring scalability, reliability, and maintainability.
• Translate analytical findings into clear, compelling narratives through data visualization, written deliverables, and presentations for technical and non-technical stakeholders.
• Partner with stakeholders across the organization to uncover opportunities where data and machine learning can improve decision-making, automation, and customer or operational outcomes.
• Propose innovative analytical approaches and new ways of framing problems using data mining, visualization, and experimentation.
• Contribute to the development and standardization of data science tools, frameworks, processes, and best practices.
• Stay current with emerging trends, techniques, and technologies in machine learning and analytics through ongoing learning and industry engagement.

What You Bring
Mindset & Collaboration

• Passion for applying advanced analytics and machine learning to solve complex, real-world business problems.
• Intellectual curiosity and a strong interest in exploring new AI/ML techniques and understanding when and how to apply them effectively.
• A hands-on builder mentality, with experience taking solutions from early exploration through deployment and adoption.
• Comfort working in cross-functional, multidisciplinary teams that include engineers, analysts, and business leaders.
• Strong communication skills, with the ability to explain complex concepts, assumptions, and trade-offs to diverse audiences.
• Experience providing technical guidance and mentorship to other data scientists while remaining an active contributor.

Qualifications & Experience

• Advanced degree in Statistics, Computer Science, Engineering, Applied Mathematics, or a related field, with experience commensurate to degree level.
• 3+ years of hands-on experience developing, validating, and deploying machine learning models in applied settings.
• Strong foundation in probability, statistics, experimental design, and statistical modeling.
• Proficiency in Python and experience with common data science and ML libraries (e.g., pandas, Num Py, scikit-learn or similar).
• Experience working with a variety of machine learning techniques, including supervised and unsupervised methods, and understanding their practical strengths and limitations.
• Hands-on experience with data wrangling techniques, including fuzzy matching, text processing, and working with large or distributed datasets.
• Familiarity with core software engineering and data science best practices such as version control, testing, logging, and reproducibility.
• Proven analytical and problem-solving skills with a high degree of accuracy and attention to detail.
• Prior experience in regulated industries such as insurance or financial services is a plus.

Compensation: $135,000 to $150,000 annually
Compensation is based on a range of factors that include relevant experience, knowledge, skills, other job-related qualifications.