Sr Lead Data Scientist

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Description of Role/Responsibilities:

Beacon Hill is looking for an experienced Sr lead Data Scientist to support one of our clients in a high-impact, senior-level role. This individual will serve as both a technical leader and hands-on contributor, responsible for designing, building, and delivering advanced data science solutions from the ground up. The ideal candidate brings deep expertise in forecasting, modeling, and pricing or revenue optimization systems, with the ability to quickly step in and drive meaningful results with minimal ramp-up.

This role requires a highly skilled professional who can operate at scale-effectively functioning as a senior lead while taking on complex individual contributor responsibilities. You will work across the full data science lifecycle, translating ambiguous business challenges into actionable, data-driven solutions, while partnering with stakeholders to deliver measurable impact. Success in this role requires strong ownership, speed, and the ability to thrive in a fast-paced, high-demand environment where priorities evolve quickly.

Candidates must be highly proficient in modern data science tools and technologies, including generative coding assistants, and leverage them to enhance efficiency and output. This is an opportunity for a top-tier data science professional to step into a critical role, drive innovation, and make an immediate difference.

Required Skills

10+ years of experience in data science roles with progression into senior/principal-level ownership, including leading complex initiatives end-to-end.

Proven ability to write production-level code from scratch and deliver scalable, high-impact solutions.

Advanced proficiency in Python (or R) for modeling, data manipulation, and production workflows; strong proficiency in SQL for complex data analysis.

Deep expertise in forecasting, statistical modeling, measurement methodologies, and mathematical optimization techniques.

Strong optimization research background required, with hands-on experience developing and implementing optimization models in enterprise-scale environments.

Hands-on experience building and deploying pricing, revenue optimization, or decisioning systems.

Experience building predictive/statistical models and feeding outputs into optimization engines to drive prescriptive decision-making.

Demonstrated experience working on prescriptive analytics teams, with a focus on optimization rather than traditional predictive machine learning alone.

Experience with optimization techniques such as linear programming, mixed-integer programming, nonlinear optimization, simulation optimization, or related methodologies.

Demonstrated ability to lead technical delivery while actively contributing as an individual contributor.

Experience owning the full data science lifecycle (data acquisition, feature engineering, modeling, validation, deployment).

Ability to work with large, complex, and unstructured datasets, including data cleansing, transformation, and validation.

Strong background in ETL/ELT processes and building repeatable data pipelines.

Ability to quickly ramp and deliver impact with minimal oversight or training.

Experience using modern development tools, including coding assistants and collaborative workflows.

Strong analytical and problem-solving skills, with the ability to translate ambiguous business problems into data-driven solutions.

Proven ability to manage multiple high-priority initiatives in fast-paced environments.

Strong communication skills with the ability to influence stakeholders and clearly explain complex models and trade-offs.

Must be highly proficient with generative coding assistant tools and actively leverage them to improve development speed, efficiency, and code quality; candidates without practical experience using these tools will not be considered a fit.

Experience delivering enterprise-level optimization solutions that directly impact pricing, revenue management, operational efficiency, or business performance.

Desired Skills

Advanced degree (Master’s or PhD) in Data Science, Statistics, Mathematics, Operations Research, Industrial Engineering, Computer Science, or a related quantitative field.

Specialization or academic background in Operations Research, Optimization, Decision Science, or related disciplines.

Experience operating at a level where you can replace or augment multiple data scientists, delivering both strategic direction and execution.

Background in revenue management, pricing strategy, or similar optimization domains.

Experience with dimensional data modeling and building analytics-ready data structures.

Hands-on experience with cloud platforms and modern data stacks (e.g., AWS, Azure, GCP, Snowflake, Databricks).

Familiarity with distributed data processing frameworks (e.g., Spark) and large-scale data environments.

Experience with workflow orchestration and transformation tools (e.g., Airflow, dbt, Data Factory).

Experience supporting or leading machine learning model deployment in production environments.

Experience integrating machine learning, forecasting, or statistical models with optimization frameworks to support prescriptive business decisions.

Experience mentoring or informally leading teams while remaining hands-on.

Ability to step into high-demand environments and relieve bandwidth constraints for senior leadership.

Experience in high-impact, high-visibility roles where speed and quality of output are critical.

Familiarity with data visualization tools (Tableau, Power BI) for communicating insights.

Strong intellectual curiosity and passion for deep data exploration and continuous learning.

Comfortable working in onsite or remote environments, with flexibility based on business needs.

Experience supporting pricing optimization, revenue management, inventory optimization, resource allocation, or other large-scale enterprise optimization initiatives

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