Your Contribution
Highly experienced Senior Data Scientist to design, deliver, and scale high impact, production grade data science and applied AI solutions. This role requires deep expertise in statistical analysis, machine learning, and Generative AI, along with strong ownership of the end-to-end data science lifecycle.
You will work closely with business, analytics, engineering, and platform teams to translate complex business needs into scalable, reliable, and measurable data science solutions. While collaborating cross functionally, you will retain primary accountability for solution design, model development, deployment, and continuous optimization.
Your Experience And Qualification
Educational Background
B.Tech/B.S./ M.S. in Computer Science, Statistics, Mathematics, or a related field
Professional Experience
6–10 years of hands-on experience in data science, machine learning, or AI engineering roles
Proven experience delivering production-grade machine learning or AI solutions
Technical Skills
Strong proficiency in Python (pandas, NumPy, scikit-learn, PyTorch and/or TensorFlow)
Deep Experience With
Supervised, unsupervised, and reinforcement learning techniques
Statistical modeling, experimentation, and hypothesis testing
Strong SQL skills and experience with big data technologies such as:
Spark, Hive, Presto, Databricks, or equivalent
Experience working with cloud platforms (Azure, AWS, or GCP)
Exposure to MLOps tools and practices (model versioning, CI/CD, monitoring)
Familiarity with orchestration frameworks such as Airflow or Kubeflow
Experience with AI platforms, analytics platforms, or conversational AI systems
Key Responsibilities
Data Science & Advanced Analytics
- Translate complex business challenges into well defined, actionable data science problems
- Conduct in depth exploratory data analysis to identify patterns, drivers, and actionable insights
- Design, develop, and validate advanced statistical and machine learning models
- Select appropriate algorithms based on data characteristics, business objectives, and constraints
- Perform rigorous model evaluation, error analysis, and performance interpretation
- Clearly communicate findings, assumptions, trade-offs, and limitations to both technical and non-technical stakeholders
- Continuously monitor, refine, and retrain models based on performance, feedback, and evolving requirements
Productionization & Collaboration
- Partner with business, analytics, and platform teams to enable seamless model deployment and integration into production environments
- Establish and promote data science with the best practices, standards, and documentation
- Support MLOps processes including versioning, CI/CD, monitoring, and observability
- Ensure scalability, reliability, and maintainability of deployed solutions
Generative AI & Applied AI
- Design, implement, and evaluate Generative AI solutions for real-world business use cases
- Apply and assess qualitative and quantitative metrics to evaluate GenAI system performance
- Develop solutions leveraging embeddings and vector representations for semantic search and similarity
- Demonstrate strong understanding of tokens, context windows, and their impact on quality, latency, and cost
- Design and apply RAG (Retrieval-Augmented Generation) and CAG (Context/Cached-Augmented Generation) patterns
- Apply effective prompting techniques including zero-shot and one-shot strategies
- Control and reason about temperature, randomness, and output variability
- Identify causes of hallucinations and implement mitigation strategies
- Implement and evaluate guardrails at prompt, retrieval, and output levels
- Understand agent-based concepts including A2A (Agent-to-Agent) interactions
- Demonstrate familiarity with model–context–memory management concepts (MCP), as applicable
Machine Learning & Deep Learning
- Develop and evaluate ML solutions across multiple domains, including:
- Forecasting and time series analysis
- Classification and regression
- Clustering and segmentation
- Natural Language Processing (NLP)
- Computer Vision (CV)
- Apply supervised, unsupervised, and semi-supervised learning techniques
- Demonstrate conceptual and applied understanding of deep learning architectures:
- ANN / MLP
- RNN / LSTM / GRU
- CNN
- High-level understanding of transformer-based models
- Conduct hyperparameter tuning, model optimization, and robust validation
Leadership & Mentorship
- Mentor junior data scientists and provide technical leadership across the team
- Act as a trusted advisor for data science and AI initiatives
- Champion high standards for analytical rigor, ethics, and reproducibility
Minimum Requirements
- Strong communication, stakeholder management, and organizational skills
- Self-motivated, customer-focused, and detail-oriented mindset
- Knowledge of ERP systems (SAP) – strongly preferred
- Six Sigma Yellow Belt or Green Belt certification – a plus
- ITIL certification – a plus
Our Offering.
We will provide a collaborative environment working on exciting assignments, along with ongoing personal & career development opportunities.
We encourage you to apply even if you don't meet every single requirement. You may just be the right candidate for this or other roles!
After receiving your online application, the interview process will typically be, once your profile will get shortlisted.
#DiversityMatters with our inclusive culture, we welcome applications from all social, religious & ethnical backgrounds, disabilities both mental and physical, identities (gender) and neurodivergent people.
Do you have any questions
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