About the role
- Design, develop, and deploy machine learning models that meet business needs and deliver measurable impact.
- Manage the end-to-end lifecycle of AI/ML models, including data gathering, feature engineering, model training, testing, and deployment.
- Collaborate with data engineers, product managers, and business stakeholders to understand project requirements and deliver impactful solutions.
- Guide the development and optimization of large-scale data pipelines to support model training and deployment.
- Develop, Implement, and enhance models in areas such as predictive analytics, GenAI, natural language processing (NLP), deep learning, and recommendation systems.
- Ensure the scalability, efficiency, and accuracy of machine learning models in production environments.
- Mentor and guide junior data scientists and ML engineers, fostering a culture of learning and innovation.
- Stay up-to-date with the latest industry trends, technologies, and research in machine learning, AI, GenaI and data science, and apply relevant insights to projects.
- Communicate technical concepts and results effectively to business stakeholders and senior leadership.
- Work closely with DevOps and engineering teams to manage the lifecycle of ML models, including versioning, monitoring, and maintenance.
- Contribute to the strategic direction of the AI/ML/GenaI practice within the organization.
About you
6+ years of experience in data science / machine learning, with at least 2 years as a Senior Data Scientist/ML Engineer with the following skills and tools/technologies:
- Bachelor s/PG degree in Engineering, Computer Science, Data Science, Statistics or a related field with a focus on analytics skills.
- Proven experience leading end-to-end machine learning projects, from conceptualization to deployment.
- Strong knowledge of machine learning algorithms, model evaluation metrics, and best practices for model deployment.
- Proficiency in Python used in data science and ML. Familiarity with big data technologies and cloud services.
- Working knowledge of a variety of machine learning techniques and concepts (regression, time series, classification, Ensemble modeling, Gradient, Boosting, stacking, clustering, decision trees, Neural Networks, image/text processing/NLP, AI, XAI, Transformers/LLM/GenAI and RAG etc.)
- Solid understanding of statistical analysis, data mining, and data visualization techniques.
- Good to have experience with MLOps practices to ensure the smooth operation of models in production.
- Hands-on experience with machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn, pretrained ML models, Transformers, RAG ), and advanced SQL.
- Ability to tackle complex problems, break them down into actionable steps, and deliver practical solutions.
- Excellent verbal and written communication skills, with the ability to translate complex data insights into clear and actionable recommendations.
- Understanding of how to align machine learning and AI solutions with business goals.
- Ability to work cross-functionally with teams across engineering, product, and business domains.
- Experience with cloud platforms, including Google Cloud Platform (GCP) , Azure for machine learning / GenAI.
