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About the role
About Sugar.fit Founded in 2021, Sugar.fit provides a personalized, evidence-based path to type 2 and pre-diabetes reversal and management. Leveraging a precision health platform that combines CGM sensors, machine learning, and medical science, we help improve metabolic health through precision nutrition, progressive fitness, and behavioral health coaching. Our research-driven treatment plan blends innovative technology, compassionate diabetes experts, and personalized strategies to empower people to lead a normal, healthy life. Function: Data Science and Analysis – Data Science / Machine Learning Responsibilities Lead and mentor a team of data scientists and analysts, providing technical guidance, career development, and performance feedback. Collaborate with cross-functional teams to align data science initiatives with business objectives. Analyze raw data: assess quality, cleanse, and structure it for downstream processing. Design and deploy accurate, scalable, and high-performance prediction algorithms. Drive end-to-end ownership of machine learning models, from research to production. Collaborate with engineering to transition analytical prototypes into production-ready solutions. Generate actionable insights for business strategy and product improvements. Stay updated on emerging technologies and industry trends, introducing best practices to the team. Requirements Experience: 4-7 years in quantitative analytics, data modeling, or machine learning, including 2+ years of team leadership/management experience . Strong problem-solving skills with a focus on product development. Expertise in statistical programming languages (Python, R, SQL, etc.) to manipulate and analyze large datasets. Strong understanding of a variety of machine learning techniques (clustering, decision trees, neural networks, NLP, etc.) and their practical pros/cons. Proven track record of deploying ML models at scale in production environments. Excellent communication skills for coordinating across teams and presenting to leadership. Proficiency in Big Data frameworks and visualization tools (Hadoop, Spark, Cassandra, Tableau, Power BI, etc.). Familiarity with cloud environments (AWS, GCP, Azure) is a plus. Bachelor's/Master's/Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.