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Popular Data Scientist title and brand visibility balanced by seniority and specialized ML requirements.
Highly technical ML/AI requirements mean moderate transferability across industries with ML capabilities.
Explicit 8-10 years plus mandatory ML, DL, deployment and platform skills increase shortlisting rigidity.
Develop and implement large-scale data science products integrating metaheuristic and multi-level forecasting algorithms to predict user trends and derive insights from structured and unstructured data.
Build and optimize machine learning and deep learning models including decision trees, SVM, GBMs, CNN, RNN, LSTM for pattern mining, classification, regression, and recommender systems.
Manage end-to-end deployment of data science solutions using cloud and visualization tools such as AWS SageMaker, MLflow, Databricks/Spark, Snowflake, and Tableau.
5+ years of total work experience, with 4+ years relevant to data science, ideally 7+ years preferred.
Proficiency in Python and SQL is mandatory; experience with machine learning algorithms (Decision Trees, SVM, GBMs, regression) and deep learning frameworks (TensorFlow, Keras, PyTorch).
Experience with cloud and big data platforms including Databricks/Spark, Snowflake, MLflow, and AWS SageMaker or equivalent deployment platforms.
University degree is preferred; specific degree not explicitly mentioned in the JD.
Experienced in building and deploying complex, scalable machine learning and deep learning models for real-world data science products in a production environment.
Hands-on experience with advanced analytics, user profiling, optimization, and knowledge of large language models or prompt engineering is a plus.
Comfortable working with large datasets and leveraging a combination of cloud platforms, data pipelines, and visualization tools to deliver actionable insights.