Senior Data Scientist

Job Description

Responsibilities: Feature Engineering and Data Preprocessing: Work with raw data to perform data cleaning, transformation, and feature engineering to prepare datasets for analysis. Collaborate with cross-functional teams to gather and understand data requirements. Model Deployment and Monitoring: Maintain, optimize, and scale recommendation engines used in FMCG or consumer goods environments to ensure high availability, accuracy, and low-latency performance across channels. Continuously improve recommendation models using advanced ML techniques such as collaborative filtering, content-based filtering, matrix factorization, and hybrid recommender systems Model Collaboration and Communication: Collaborate with product, marketing, and engineering teams to integrate recommendations into consumer-facing applications, personalization engines, or B2B retail platforms. Communicate complex findings and insights in a clear and understandable manner to both technical and non-technical stakeholders. Continuous Learning and Research: Stay abreast of industry trends and advancements in data science and machine learning. Continuously enhance skills through training and self-directed learning. Qualifications: Education and Experience: Bachelor's or master's degree in computer science, Statistics, or a related field. years of proven experience as a Data Scientist. Technical Skills: Strong proficiency in Python for data analysis, statistical modeling, and machine learning (e.g., pandas, scikit-learn, statsmodels, NumPy). Hands-on experience in developing recommendation engine & Graph embedding algorithms like GraphSAGE. Hands-on experience on handling Large Dataset using Azure Databricks and Azure Cloud is must. Strong understanding on Model Evaluation parameters. Experience with machine learning frameworks (e.g. PyTorch, scikit-learn). Strong knowledge of statistical analysis and data visualization tools. Problem-Solving: Ability to translate business problems into analytical frameworks and develop data-driven solutions. Communication Skills: Strong interpersonal and communication skills with the ability to explain complex concepts to non-technical stakeholders. Team Collaboration: Demonstrated ability to work effectively in a collaborative team environment.