Skills Required: Must have experience with: Implement data science solutions on Cloud environments: Google Cloud, Azure; Coding in object-oriented language: Python, C++, Use statistical software: R; Use ML and Deep learning frameworks: Scikitlearn, TensorFlow, PyTorch, Keras; Computer Vision:ResNet50,fast.ai for image segmentation and object detection; Big Data technologies: BigQuery, Spark, Hive, Hadoop for Data Integration: Databricks, Snowflake, Collibra; Feature engineering, extraction, reduction techniques using Python, R; Exploratory data analysis with data warehouses via database querying language: SQL, setting up ETL jobs with Glue, Knime, Alteryx, Data Visualization using Python, Tableau, PowerBI, Matplotlib, Seaborn, ggplot, Spotfire for insights to stakeholders; Implement supervised and unsupervised ML techniques: K-means clustering,Principal Component Analysis,Hierarchical Clustering,ARIMA,Hierarchical linear modeling and Gradient Boosting on Python,R, operational research solutions with optimization libraries (PuLP,CPlex), optimization and neural nets,natural language processing with gensim,BERT, deploy time series forecasting solutions with ML techniques like DeepAR on Python and R; Identify model evaluation metrics and apply best techniques for model testing and tuning to assess accuracy, fit, validity, robustness using advanced statistics; Translate business problems into production grade scalable data science solutions with; MLflow,Docker,Kubernetes implementation generating actionable insights. Minimum education and experience required: Master’s degree or the equivalent in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field plus 3 years of experience in analytics or related experience; OR Bachelor’s degree or the equivalent in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field plus 5 years of experience in analytics or related experience.