Lead Data Scientist

About RNTBCI:

Renault Nissan Technology & Business Centre India (RNTBCI) is a captive automotive technology and business centre supporting Renault & Nissan globally.

Established in 2007, it is located in Mahindra World City, Chennai, India and employs over 8,000 employees. RNTBCI was bestowed with ISO 9001:2015 certification in 2019 and ISO/IEC 27001:2013 certification in 2020. RNTBCI contributes to technology requirements with dedicated teams for, Product Engineering, Process Engineering, Research & Advanced Engineering, After-Sales Engineering, Business requirements.

Required Primary skills:

Python, Machine learning algorithms, NLP, Gen AI, Models, ML libraries & frameworks, Rasa

MLOPS, Statistics, GCP/AWS Cloud

Job Description:

1) Identify opportunities in AI and work with cross-functional/vertical teams to implement AI solutions & enhance offerings.

2) Collaborate with internal teams to define AI requirements and develop roadmap that aligns with corporate statergy

3) Team Leadership: Lead a multidisciplinary team of AI engineers, data scientists, researchers, and analysts. Provide guidance, mentorship, and support to ensure the team's success in delivering high-quality AI solutions.

4) Project Management: Oversee the planning, execution, and delivery of AI projects. Monitor project timelines, budgets, and resources to ensure successful outcomes while adhering to quality standards.

5) Collaboration and Stakeholder Engagement: Work closely with internal stakeholders such as executives, department heads, and cross-functional teams to understand their AI needs and requirements.

6) Research and Development: Stay abreast of the latest advancements in AI technologies, tools, and methodologies. Lead research efforts to explore new opportunities and innovative solutions.

7) Data Governance and Ethics: Establish and enforce data governance policies, ensuring compliance with regulatory requirements and ethical standards.

8) Promote responsible AI practices and mitigate potential risks associated with data privacy and bias.

9) Performance Evaluation: Define key performance indicators (KPIs) and metrics to measure the effectiveness and impact of AI initiatives. Regularly evaluate performance against these benchmarks and implement improvements as needed.

10) Training and Development: Provide training programs and resources to upskill employees in AI-related competencies. Foster a culture of continuous learning and knowledge sharing within the organization.

11) Communication and Reporting: Prepare regular reports and presentations for senior management and stakeholders, communicating progress, insights, and recommendations related to AI initiatives.

12) Working experience on cloud technologies would be an added advantage.

For more Information, Kindly go through our website - https://rntbci.in