We are seeking a highly motivated and skilled Data Scientist to join our team in the retail industry. The ideal candidate has at least 2 years of experience in data science, with expertise in data analysis, predictive modeling, and machine learning. Exposure to MLOps, feature engineering, and data engineering workflows will be considered a plus.

Key Responsibilities:

• Data Analysis: Collect, preprocess, and analyze large datasets to identify trends and actionable insights for retail business challenges.
• Model Development: Design, train, and deploy machine learning models for tasks such as demand forecasting, customer behavior analysis, and inventory optimization.
• Collaboration: Partner with cross-functional teams, including data engineers and business stakeholders, to translate requirements into data-driven solutions.
• Visualization and Communication: Present insights and findings through visualizations and dashboards to inform decision-making.
• Innovation: Stay updated on the latest tools and techniques in data science and retail analytics.

Optional Responsibilities (if experienced):

• Feature Engineering:
• Engineer and optimize features to improve machine learning model performance.
• Automate feature extraction pipelines for scalable workflows.
• MLOps:
• Contribute to the deployment, monitoring, and retraining of machine learning models in production environments.
• Data Engineering:
• Assist in designing and maintaining data pipelines and ensuring data quality.

Requirements

• Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Experience: At least 2 years of experience in data science or a related field.
• Technical Skills:
• Proficiency in Python for data analysis and machine learning.
• Strong SQL skills for managing and querying large datasets.
• Experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
• Knowledge of data visualization tools (e.g., Tableau, Power BI, matplotlib).
• Soft Skills: Strong problem-solving, communication, and teamwork abilities.

•  Preferred (Optional) Qualifications:

• Exposure to MLOps tools (e.g., MLflow, Kubeflow, AWS SageMaker).
• Familiarity with data engineering tools (e.g., Apache Spark, Kafka, Airflow).
• Experience in building real-time analytics or personalization systems.