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!