Data Science Team Lead

Posted 7 days agoViewed

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💎 Seniority level: Lead, 5+ years

📍 Location: Rīga, Tallinn, Barcelona

🔍 Industry: Software Development

🗣️ Languages: English

⏳ Experience: 5+ years

🪄 Skills: AWSLeadershipPythonSQLCloud ComputingData AnalysisMachine LearningNumpyProduct ManagementPyTorchCross-functional Team LeadershipAlgorithmsData engineeringData sciencePandasTensorflowCommunication SkillsAnalytical SkillsCI/CDProblem SolvingRESTful APIsMentoringCross-functional collaborationData visualizationTeam managementStrategic thinkingData modelingData management

Requirements:

5+ years of experience in data science, machine learning, or AI.
2+ years of proven leadership experience, managing a team of data scientists and overseeing projects from ideation to deployment.
Experience in a marketplace or product-driven company.
Hands-on expertise in model development, optimization, and deployment in production.
Familiarity with MLOps best practices to ensure scalable and maintainable ML workflows.
Proficiency in Python-based ML frameworks for model development and analysis.
Experience with SQL for data querying and manipulation.
Knowledge of cloud platforms, with AWS preferred, for ML model deployment.
Proven ability to solve real-world ML problems at scale.
Strong cross-functional collaboration skills, working with product, engineering, and business teams.
Ability to balance hands-on technical work with strategic planning and team leadership.
Experience in hiring, scaling teams, and fostering a high-performance ML culture.

Responsibilities:

Lead a team of 7 data scientists, providing functional guidance and direction.
Organize regular 1:1s with data scientists to ensure alignment and support.
Mentor and coach data scientists to strengthen their technical expertise and business understanding.
Conduct performance reviews, set development goals, and provide career growth guidance.
Establish and evaluate optimal team rituals to enhance project delivery.
Increase the organizational impact of the machine learning team by improving collaboration and innovation practices.
Stay up to date with advancements in machine learning and AI, driving their application within the business.
Collaborate with the MLOps team to ensure infrastructure and ML platforms meet data science needs.
Define and track KPIs for machine learning projects, proactively addressing any issues.
Work closely with ML product managers to: Define the vision of machine learning within the company. Explore and discuss potential ML applications for business challenges.

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