Staff R&D Engineer – Data Science (Pittsburgh, MA)

Smith+Nephew•Andover, MA

1d•Hybrid

About The Position

Staff R&D Engineer – Data Science (Pittsburgh, PA) Life Unlimited. At Smith+Nephew we design and manufacture technology that takes the limits off living. We’re seeking a Staff R&D Engineer – Data Science who will play a key role in developing and implementing advanced AI solutions across Smith+Nephew’s product portfolio. In this hands‑on role, you will investigate cutting‑edge AI techniques, build prototypes, and translate emerging research into functional proof‑of‑concept systems that directly support S+N’s product roadmap. You will work at the forefront of AI innovation—designing models, building pipelines, validating performance, and ensuring new technologies can be effectively and safely applied in orthopedic, wound care, and sports medicine applications. As a technical leader, you will explore and evaluate state‑of‑the‑art AI methods—generative models, LLMs, deep learning architectures, multimodal systems, and more—and turn this exploration into concrete, testable prototypes. You’ll collaborate closely with R&D, engineering, and product teams to design experiments, implement algorithms, create datasets, and build scalable tools that demonstrate feasibility and accelerate adoption of next‑generation AI capabilities. Your hands‑on expertise will help drive the development of real‑world AI applications that measurably improve patient outcomes.

Requirements

Bachelor's Degree in Data Science, Artificial Intelligence, Computer Science, or a related field required with 8+ years of experience applying AI/ML in R&D or product innovation.
Demonstrated experience leading AI initiatives in a research or commercial environment.
Expertise in machine learning, deep learning, and statistical modeling.
Advanced programming skills in Python and familiarity with frameworks such as Scikit-learn, TensorFlow, NumPy, Pandas, PyTorch, or Keras.
Strong understanding of data architecture, preprocessing, and feature extraction techniques.
Proven ability to translate AI capabilities into real-world solutions with measurable business impact.
Excellent communication skills and the ability to influence cross-functional stakeholders and decision-makers.

Nice To Haves

PhD in Data Science, Artificial Intelligence, Computer Science, or a related field preferred
Experience working with healthcare or regulated products is a plus.

Responsibilities

Researching and evaluating cutting‑edge AI technologies—including generative AI, LLMs, and deep learning—and rapidly translating them into functional prototypes and proof‑of‑concept implementations.
Building, training, and fine‑tuning machine learning and deep learning models using frameworks such as PyTorch, TensorFlow, Keras, Scikit‑learn, and other emerging toolkits.
Designing and implementing scalable data pipelines, preprocessing workflows, and feature engineering steps that support experimentation and model development.
Developing end‑to‑end prototype systems—including data ingestion, model execution, evaluation, and visualization—to demonstrate feasibility and performance of new AI capabilities.
Running structured experiments, benchmarking models, and validating technical performance using statistical methods, simulation tools, and experimental datasets.
Creating internal tools, scripts, and interfaces that allow team members to test, interact with, and evaluate AI‑driven functionalities.
Collaborating with R&D, engineering, and product teams to integrate prototype AI components into existing software environments, instruments, or clinical workflows.
Documenting technical findings, experiment results, and model performance to support next-step development, regulatory considerations, and engineering decisions.
Mentoring team members on implementation techniques, best practices for data preparation, model tuning, and software engineering for AI systems.
Communicating complex technical results—model behavior, limitations, assumptions, and experimental outcomes—to stakeholders across technical and non-technical teams.
Work in a collaborative, fast-paced, multidisciplinary environment.