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Data Science Manager

icon building Company : Nissan
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Job Description - Data Science Manager

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
Trivandrum Kerala India
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