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. Must have at least 7+ years of hands-on experience in data science or a related field, with a demonstrable track record of delivering machine learning solutions in production.
. Should be proficient in Python and relevant data science libraries such as scikit-learn, PyTorch, or TensorFlow.
. Knowledge of geospatial tools and frameworks such as GeoPandas, QGIS, PostGIS, or ArcGIS is a bonus.
. Strong skills in SQL and experience with cloud data platforms (e.g. AWS, GCP, or Azure) are expected.
. Must be familiar with the full ML lifecycle, from data wrangling and feature engineering through to model evaluation, deployment, and monitoring.
. Must be comfortable with more advanced ML techniques such as ensemble learning, regularization, agent-based modelling, forecasting, etc.
. Prior experience working with geospatial data and tools is strongly preferred, as is experience in domains involving demographic modeling, urban planning, or public sector analytics.
Job ID: 151522655
Skills:
causal inference , Clustering, Sql, Python, Uplift modelling, Time-series, Demand Forecasting, Instrumental variables, Difference-in-differences, Regression, Synthetic control, Classification, Price-elasticity estimation, Experiment design, Statistical ML modelling
Skills:
Github, Machine Learning, Hadoop, Artificial Intelligence, Power Bi, Sql, Hive, Bitbucket, Spark, Gitlab, Databricks, Azure, Python, AWS, Airflow, Generative AI, MLflow
Skills:
causal inference , Databricks, Python, Sql, DiD, Forecasting, DoubleML, R, Synthetic Control, AI-enabled solutions
Skills:
Hadoop, Data Warehousing, Sql, Mapreduce, Tensorflow, Pytorch, Python, data analysis tools, scikit-learn, Hugging Face Transformers, Flink, large-scale ML models, ETL processes, ML pipeline orchestration platforms
Skills:
Predictive Modelling, Sql, Python, graph analytics, experimentation frameworks, anomaly detection