Data Science Developer

Your Role and Responsibilities

• 3+ years of experience in a Data Science, machine learning or a related field.
• Strong hands-on experience in Machine Learning and Statistics focusing on structured and unstructured data problems.
• Practical experience in several of the following areas: time series forecasting, clustering and classification techniques, regression, boosting algorithms, optimization techniques, NLP, recommendation systems, ElasticNet Excellent programming skills preferably in Python/Py spark and SQL
• Understanding of developing, implementing, deploying machine learning models on the cloud platforms(Azure, AWS, GCP) by using AWS/Azure Machine Learning, Data bricks, or other relevant cloud services
• Integrate machine learning models into existing systems and applications, ensuring seamless functionality and data flow
• Understanding of developing and maintaining MLOps pipelines for automated model training, testing, deployment, and monitoring
• Understanding of monitoring and analysing model performance, providing reports and insights to stakeholders as needed
• Familiarity with data processing and storage tools, such as SQL, Hadoop, or Spark Advanced engineering abilities to deliver flexible and scalable end-to-end machine learning solutions.
• Exposure to data visualization software and packages (Power BI, Tableau, matplotlib, d3) Understands challenges in business area, applicability of relevant data science disciplines, and system interactions.
• Excellent written and verbal communication skills, confidence in presenting ideas and findings to stakeholders, and ability to do so at the right level of detail.

Required Technical and Professional Expertise

• Engineering Graduate from a reputed institute and/or Masters in Statistics, MBA
• 3+ years of Data science experience
• Strong expertise and deep understanding of machine learning.
• Strong understanding of SQL & Python. Knowledge of Power BI or Tableau is a plus
• Exposure to Industry specific (CPG, Manufacturing) use cases is required
• Strong client-facing skills
• Must be organized and detail oriented.
• Excellent communication and interpersonal skills

Preferred Technical and Professional Experience

• Strong foundation in Supervised and Unsupervised Learning (Regression, Classification, Clustering, etc.).
• Proficiency in Ensemble Learning (Random Forest, Gradient Boosting, XGBoost, LightGBM, etc.).
• Experience in fine-tuning Large Language Models (LLMs) and working with open-source models (Llama, GPT, BERT, etc.).
• Familiarity with Prompt Engineering, RAG (Retrieval-Augmented Generation), and Fine-tuning techniques.
• Hands-on experience with Cloud Platforms (AWS, GCP, Azure) for ML model deployment.

Familiarity with MLOps and Model Deployment using Kubernetes, Docker, and MLflow.