Data Scientist

5+ years ofexperience in product analytics or data science. Practical project experiencein advanced data analysis, statistical modeling, and machine learning conceptsis essential. Education: Background in Computer Science/Computer Applications or any quantitative discipline (Statistics, Mathematics, Economics/Operations Research, etc.) from a reputed institute. Analytical Skills: Analytical thinker with a passion for using data and scientific methods to solve business problems. Programming Proficiency: Proficient with common analytical programming languages e.g. Python; expert in SQL. Experience with writing production-ready code in Python and automation. Solid understanding of data transformations and analytics functions using tools/languages like Pandas, Sklearn, SQL, and Spark. Experience with tools like git, mlFlow, Airflow, Cron, Docker, and Cloud Platforms such as AWS/GCP is a plus. Technical Expertise: Strong proficiency in core Data Science, Machine Learning, and Statistical modeling. In-depth experience in techniques from time-series forecasting and/or causal inference. Experience with Experiment Design, Natural Language Processing, Neural Network architectures, Recommendation Systems, and Large Language Models, Generative AI (LLM) is highly desirable. Advanced Techniques: Deep knowledge of techniques such as Linear Regression, Gradient Descent, Logistic Regression, Forecasting Time Series, Cluster Analysis, Decision Trees, Linear Optimization, and Text Mining. Specific Skills: Must have Semantic Modeling & NER (Named Entity Recognition) experience. Pre-trained Models: Experience working with pre-trained models, awareness of the state-of-art in embeddings and their applicability for use cases. NLP/NLG/NLU Experience: Must have strong experience in NLP/NLG/NLU applications using popular deep learning frameworks like PyTorch, TensorFlow, BERT, Langchain, and GPT (or similar models). Generative AI Exposure: Must have exposure to Gen AI models (LLMs) like Mistral, Falcon, Llama 2, GPT-3.5 & 4, and Prompt Engineering. Cloud Experience: Experienced in developing and deploying data science models, especially in the Cloud environment. Must have worked using Azure services or AWS SageMaker for ML & GenAI projects. Consultative Skills: Demonstrated ability to engage with client stakeholders at multiple levels and provide consultative solutions across different domains. Benefits Competitive salaries Medical Insurance Employee Referral Bonuses Performance Based Bonuses Flexible Work Options & Fun Culture Robust Learning & Development Programs In-House Technology Training