Senior Data Scientist — Core AI Research and Development

Preferred: Time-zone overlap with India working hours

Important: this is a Core AI R&D role requiring both strong applied ML engineering experience and research-level scientific depth. We are looking specifically for candidates with a research scientist mindset — someone who actively reads and publishes in relevant ML domains, understands state-of-the-art literature, and has demonstrated experience translating research ideas or papers into production-grade systems. Purely applied ML, analytics, or “model usage” backgrounds will not be a fit!
 

About the Opportunity

Our client is a fast-growing technology company developing advanced AI-powered solutions for industrial analytics and operational intelligence. As they continue to expand their Core AI Research & Development team, we are seeking a Senior Data Scientist with strong research and applied experience in one or more of the following areas:

• Time-series modeling for industrial sensor data
• Reinforcement learning and prescriptive decision-making
• Knowledge representation, graph learning, and multi-modal AI systems

This role offers the opportunity to work on cutting-edge AI research while driving real-world business impact through production-grade solutions.
 

Key ResponsibilitiesTime-Series Modeling

• Develop and enhance modern deep learning models for time-series analysis using large-scale industrial sensor datasets.
• Design retraining and adaptation pipelines to maintain model performance as data evolves over time.
• Apply transfer learning and low-label adaptation techniques to support new equipment types and sensor sources.
• Improve prediction accuracy across multiple asset categories.
   

Reinforcement Learning & Prescriptive Intelligence

• Design reinforcement learning frameworks that generate actionable operational recommendations.
• Develop optimization approaches that balance multiple business and operational constraints.
• Implement preference-learning techniques leveraging expert feedback and validated operational outcomes.
• Collaborate with product and engineering teams to integrate prescriptive recommendations into production environments.
   

Knowledge Representation & Multi-modal AI

• Expand and enhance domain knowledge graphs representing industrial assets, failure modes, and recommended actions.
• Apply graph-based learning methods to improve reasoning and knowledge transfer across equipment types.
• Integrate structured and unstructured data sources, including technical documentation, engineering diagrams, operator notes, and conversational data.
• Identify patterns and insights that improve model generalization across industries and customer segments.
   

Research & Collaboration

• Translate state-of-the-art research into scalable, production-ready solutions.
• Mentor junior data scientists and contribute to the growth of the research organization.
• Collaborate with cross-functional teams to bring AI innovations into customer-facing products.
• Define, monitor, and improve quality metrics for predictive and prescriptive AI systems.
• Contribute to patents, publications, and open-source initiatives where appropriate.
   

Required Qualifications, Education

• PhD preferred, or Master’s degree in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, or a related field.
• Exceptional candidates with equivalent industry experience will also be considered.
   

Technical Experience

• 5+ years of hands-on machine learning experience with deep expertise in at least one of:
  • Time-series modeling
  • Reinforcement learning
  • Graph machine learning and knowledge representation
• Strong Python programming skills.
• Experience with modern deep learning frameworks (PyTorch preferred).
• Practical knowledge of reinforcement learning methodologies and production-grade RL frameworks.
• Experience working with knowledge graphs, graph neural networks, embeddings, or related technologies.
• Familiarity with retrieval-augmented systems, vector databases, and unstructured data processing.
• Experience with cloud platforms and managed machine learning services.
• Strong software engineering practices, including version control, testing, and reproducible experimentation.
   

Soft Skills

• Strong product mindset with the ability to move research into production.
• Excellent communication skills and ability to work with both technical and non-technical stakeholders.
• Comfortable working in fast-paced environments with evolving priorities.
• Experience collaborating across distributed and international teams.

Nice to Have

• Experience in predictive maintenance, condition monitoring, industrial AI, or vibration analysis.
• Knowledge of physics-informed machine learning or causal inference techniques.
• Publications in leading ML conferences or journals.
• Experience with agentic AI systems, LLM evaluation, or advanced generative AI applications.
   

What Our Client Offers

• Opportunity to work on challenging AI research problems with direct business impact.
• Collaborative environment combining research excellence with product delivery.
• Remote-first culture with a globally distributed team.
• Exposure to large-scale industrial datasets and real-world AI applications.
• Competitive compensation package and long-term growth opportunities.
   

Looking forward to your reply!