Data Architecture – Experience in designing hybrid cloud-native on-premise and Lakehouse Data Platform implementing modern Data Science and MLOps lifecycle practices. Familiarity with Enterprise Data Modeling, Data Quality monitoring, Data Governance and Compliance policies.

Machine Learning – Experience in building and optimizing Classification, Regression, Ranking and Time-Series forecasting models for the domains of NLP, Vision and Tabular using Keras, XGBoost, Implicit, BERTopic, Spark ML, etc. Keen on coding robust, highly parallelizable, and memory-efficient algorithms using PySpark and Dask.

Data Science – Experience in Data Mining, Collection, Cleaning, Profiling, and Feature Engineering using PySpark, Pandas, Polars, BS4, Map/Reduce and etc. Ability to apply advanced analytical methods such as clustering and regression analysis, spatio-temporal modeling, sentiment and semantic mining, association rule and decision tree learning, topic modeling, dimensionality reduction and multivariate statistics using PCA/LDA, Randomized Controlled Trials experiment design and hypothesis testing, etc.

ML Ops – Experience in setting up fully Ansible-automated on-premise cloud-native Data Platforms with Jupyterhub, Airflow, MLflow, Minio. Keen on managing Linux environments, shell scripting and setting up clusters of microservices with CI/CD using Docker, Jenkins, Github Actions and K8s. Familiarity with Kubernetes, Prometheus, Grafana and Terraform.