Data Science and ML Manager
Nissan Motor Corporation
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 solution
sMLOps: Ability to build reusable model pipelines and manage deployments using MLflow and Docke
rBehavioural 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) – DASC
AAWS Certified Machine Learning – Specialt
yMicrosoft Certified: Azure AI Engineer Associat
eCoursera/edX Data Science Specializations (e.g., IBM, Stanford, Harvard)
Data Engineering Certification
Vacancy posted 3 days ago
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