At Broadridge, we've built a culture where the highest goal is to empower others to accomplish more. If you're passionate about developing your career, while helping others along the way, come join the Broadridge team.

Job responsibilities:

• Exploratory Data analysis & model pipeline: Perform Exploratory data analysis, form the solution approach and then design & develop model pipelines, use AWS Sagemaker for model experimentation, training, model promotion, etc.
• Model training with batch and real-time inference scenarios: Use machine learning and statistical modelling techniques such as Reinforcement Learning, Decision Trees, Logistic Regression, Bayesian Analysis, Neural Networks, and others to develop and evaluate algorithms to improve product/system performance, quality, and accuracy.
• Experience in analysing data quality, model quality, bias, explanability & perform required actions to improve model accuracy in production.
• Analyse & suggest efficient model retraining strategies, define validation strategy, training models & hyperparameters, verifying data quality, model quality, lineage etc.
• Stay up to date with state-of-the-art algorithms, repositories, and tools in Generative AI space (LLMs) e.g., Finetuning, quantization, pruning, etc.
• Mentor and coach junior data scientists and contribute to the continuous learning and development of the data science community.
• Staying up to with the latest AI development, and accordingly suggest improvements to our roadmap and approaches, and disseminate learnings to colleagues.
• Help establish standards and best practices around Machine Learning - training and testing framework, coding standards, engineering practices.

• Bachelor's or Master's in Computer Science, Data Science or a related Field with proven experience of 5+ years as Sr Data Scientist working on Advanced Analytics projects in Cloud based environment (preferably AWS)
• Proficient with Machine learning techniques, statistical/analytical methodologies and algorithms (e.g. classification, regression, clustering, feature selection/PCA, deep learning, time-series analysis, network analysis, hypothesis testing)
• Experience with NLP techniques
• Exhibit proficiency in Python, SQL, software engineering, along with expertise in deep learning frameworks (ML/distributed ML frameworks like TensorFlow etc)
• Familiarity with common ML packages, particularly scikit-learn, pandas, numpy, matplotlib, seaborn, XGBoost, etc. or similar. Deep learning experience (e.g. Pytorch or Tensorflow)
• Cross-functional collaboration with Product Owners, Data Engineers, SMEs, UX designers
• Understand the model build lifecycle, including feature selection and optimisation, model selection and validation, and ongoing model maintenance using ML Frameworks like Sagemaker, MLFlow
• Exceptional communication skills and ability to present findings to non-technical audience
• Experience in effective data visualization approaches and a keen eye for detail in the visual communication of data insights