The Role
The Data Science Manager leads teams in developing machine learning models and data strategies, ensuring data governance, mentoring staff, and driving actionable insights through cross-functional collaboration.
Summary Generated by Built In
Responsibilities:
- Lead and manage data science teams, overseeing the development and deployment of machine learning models and advanced analytics solutions.
- Define and execute data strategies aligned with business objectives, ensuring actionable insights drive decision-making.
- Collaborate with cross-functional teams, including engineering, product, and business stakeholders, to identify and solve complex data-related challenges.
- Ensure data integrity, governance, and security while optimizing data pipelines and infrastructure for scalability.
- Mentor and develop data scientists, providing technical guidance, performance feedback, and career development support.
- Stay updated on emerging trends, technologies, and best practices in data science and artificial intelligence (AI).
- Communicate findings effectively to both technical and non-technical stakeholders, translating insights into business impact.
Key Competencies:
- Strong problem-solving and analytical thinking skills to interpret complex data and drive insights.
- Leadership and people management abilities to guide and grow a high-performing data science team.
- Business acumen to align data science initiatives with organizational goals and drive measurable value.
- Effective communication skills for conveying technical concepts to diverse audiences.
- Decision-making capabilities based on data-driven approaches.
Technical Skills:
- Proficiency in programming languages such as Python, R, or SQL.
- Expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-Learn).
- Experience with big data technologies (Spark) and cloud platforms (AWS/ Azure/ GCP).
- Strong understanding of statistical modeling, predictive analytics, and deep learning.
- Experience with data visualization tools (Quicksight, Power BI, Matplotlib, Seaborn, Streamlit/Dash).
- GenAI: Experience with GenAI APIs, LLMs, Vectorization, Agentic AI and prompt engineering for domain-specific solutions
- MLOps: Ability to build reusable model pipelines and manage deployments using MLflow and Docker
Behavioural Competencies:
- Adaptability: Ability to pivot strategies based on evolving business needs and technological advancements.
- Learning Agility: Continuous learning mindset to keep up with emerging data science trends and methodologies.
- Teamwork: Collaborative approach to working with cross-functional teams, fostering knowledge sharing and innovation.
Certifications (Optional):
- Certified Data Scientist (CDS) – DASCA
- AWS Certified Machine Learning – Specialty
- Microsoft Certified: Azure AI Engineer Associate
- Coursera/edX Data Science Specializations (e.g., IBM, Stanford, Harvard)
- Data Engineering Certifications
Skills Required
- Lead and manage data science teams
- Proficiency in Python, R, or SQL
- Expertise in machine learning frameworks like TensorFlow
- Experience with big data technologies like Spark and cloud platforms
- Strong understanding of statistical modeling and predictive analytics
- Data visualization skills with tools like Quicksight or Power BI
- Experience with GenAI and prompt engineering
- MLOps experience with MLflow and Docker
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The Company
What We Do
Nissan is a global automotive manufacturer that designs, produces, and sells a wide range of vehicles, including cars, trucks, and SUVs, and is a key member of the Renault-Nissan-Mitsubishi Alliance.
