Manager, Data Science

Suffolk Construction•Boston, MA

10d

About The Position

Suffolk Construction is seeking a Manager, Data Science to join our Data Product team. This is a hands-on technical leadership role : you will contribute directly to high-impact modeling work while managing and developing a small team of data scientists. You will shape the team’s modeling strategies, elevate technical rigor, and drive Suffolk’s transformation from data-as-a-product (DaaP) to a data-as-a-service (DaaS) operating model - delivering scalable, reusable, and governed data and ML services across the enterprise.

Requirements

5–7+ years of hands-on experience in data science, applied machine learning, or AI, with proven end-to-end ownership of productionized ML systems.
Skilled in SQL and either Python or R (experience with scikit-learn, tidymodels , MLlib or comparable frameworks ).
Experience acting as a technical lead, senior IC, or informal mentor; ready to step confidently into a formal management role.
Comfortable balancing individual contribution with team leadership - able to switch between strategy, execution, coaching, and delivery.
Strong critical thinking, problem framing, and ability to structure ambiguous problems.
Bachelor's or Master’s degree in Data Science , Computer Science, Statistics, or related field preferred.

Nice To Haves

Experience with distributed compute environments (Spark, Databricks) is a plus.
Familiarity with the Posit ecosystem (Workbench, Connect, Quarto, vetiver) is a plus.

Responsibilities

Manage and develop a team of data scientists providing mentorship, coaching, and structured growth pathways.
Lead by example through consistent technical contribution, reinforcing standards for model design, code quality, reproducibility, and documentation.
Partner with the S enior Director, Data Product on resource planning, prioritization, and roadmap execution.
Lead the design, development, and maintenance of ML-driven products and services , including predictive models, risk scoring engines, exception detection services, and upcoming generative AI applications.
Build reusable ML components and services that can be consumed across multiple business units— aligning with a DaaS mindset.
Own the full ML lifecycle from exploration to production: data extraction, feature engineering, model training, evaluation, deployment, monitoring, and iteration.
Collaborate closely with data engineering to influence data architecture, pipeline design, data modeling, and model-serving platforms.
Translate business pri orities into product roadmaps; what problems we solve, in what order.
Communicate complex technical findings to non-technical audiences in a way that influences decisions at scale.
Orchestrate cross-functional delivery across BI, data engineering, data science, and AI .
Defines clear product boundaries and backlogs , user stories, scope, acceptance criteria, and delivers product increments end- to end .
Establish and champion best practices across MLOps , model governance, CI/CD, testing, and monitoring.
Drive continuous improvement across modeling techniques, LLM applications, and analytics tooling.
Ensure data science artifacts are built with long-term maintainability, transparency, and reusability in mind.