Data Scientist (Hybrid on-site)

Peak Technologies•Franklin, MA

1d•Hybrid

About The Position

Peak Technologies is seeking a motivated and analytical Data Scientist to help advance our data-driven strategy and support the development of intelligent automation, analytics, and AI solutions across the business. In this role, you will analyze large datasets, build predictive and generative models, and translate data into actionable insights that enhance operational efficiency and customer outcomes. You will also have opportunities to explore emerging technologies such as Large Language Models (LLMs), Large Vision Models (LVMs), and other machine learning and computer vision techniques to bridge the gap between research and business application. This position requires strong problem-solving skills, solid technical proficiency, and a genuine passion for using data to drive measurable impact in a fast-paced, collaborative environment.

Requirements

Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Artificial Intelligence, or a related field.
Minimum 1 year of experience in a data science, applied AI, or analytical role (internship or postgraduate research experience may qualify).
Proficiency in Python, R, and SQL, including experience with frameworks such as scikit-learn, TensorFlow, or PyTorch.
Strong understanding of statistical modeling, data visualization, and machine learning evaluation techniques.
Familiarity with cloud platforms (Google Cloud Platform, AWS, or Azure) and tools such as Vertex AI is preferred.
Excellent communication skills with the ability to explain complex concepts to diverse audiences.
Detail-oriented, proactive, and comfortable working collaboratively across cross-functional teams.
Passionate about innovation, continuous learning, and contributing to a data-driven culture at Peak Technologies.

Nice To Haves

Hands-on experience with Natural Language Processing (NLP) or Computer Vision tasks (e.g., text classification, image tagging, or sentiment analysis).
Exposure to data warehousing, ETL processes, or data engineering workflows.
Contributions to open-source projects or research publications in data science or artificial intelligence.

Responsibilities

Collect, clean, and preprocess large datasets from multiple sources while ensuring data accuracy, consistency, and integrity.
Apply statistical methods, hypothesis testing, and exploratory data analysis to identify trends and opportunities.
Develop and deploy machine learning and predictive models to support data-driven decision-making and product innovation.
Research, prototype, and evaluate AI and ML models, including LLMs, LVMs, and other cutting-edge algorithms.
Collaborate with engineering and data annotation teams to improve data pipelines, tagging accuracy, and supervised learning processes.
Create clear, impactful visualizations, dashboards, and reports for technical and non-technical audiences.
Design and interpret A/B and multivariate tests to measure the impact of business changes or feature deployments.
Maintain documentation for data sources, transformation logic, and model development processes.
Participate in code reviews, model evaluations, and team knowledge-sharing sessions to promote best practices.
Ensure compliance with data governance, privacy, and security standards across all data projects.