Senior Data Scientist

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.