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 : 5 to 10 years in quantitative analytics, data modeling, or machine learning, including 3+ 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.

- Bachelors/Masters/Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.