Data Science
Job Description
- Marketing Analytics Domain Understanding:- Understanding of P&C insurance marketing/ Campaign working understanding
- Participate in moderately complex initiatives to deliver insights by converting the business problem into analytical format
- Programming, reporting the finding, using end results to contribute to large scale planning and process to create decision strategy
- Generate campaign and project performance reports with ability to understand marketing concept
- Data Analytics: - Ability to do end to end programming in python/SAS to extract the results as required by the problem at hand.
- Review and analyze complex data aggregations
- Ability to self-check and QC the end results in line with changing requirements and modifications. well versed in EDA
- ML techniques: - Knowledge of Modelling based on large set of variables from varied sources
- Understanding and working knowledge of statistical and Machine learning techniques
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Key Skills
About Company
Skills:
data engineering , bayesian inference , Machine Learning, Hadoop, Big Data Technologies, Sql, Tensorflow, Nlp, Pytorch, Spark, Statistical Inference, Python, cross-validation strategies, hyperparameter optimization, Scikit-Learn, dimensionality reduction, R
Skills:
Ms Excel, SAS, Sql, Decision Trees, XGBoost, Python, Logistic Regression, Powerpoint, random forests, machine learning techniques, Customer Segmentation, R
Skills:
Python, Sql, R, Visualization Tools, Machine Learning Libraries
Skills:
triton , Cloud Infrastructure, Pytorch, Docker, Python, Apis, Cuda, Git, Linux, Distributed Systems, Kubernetes, Profiling, Continuous batching, GPU utilisation, Tokenisation, Mixed precision, KV caching, Embeddings, Sampling, Evaluation frameworks, Low-level optimisation, Attention, Inference, Decoding, vLLM, Model serving, Transformers, NCCL, Quantisation, Device placement
Skills:
Tensorflow, Pytorch, Docker, Python, AWS, Api Development, Git, Azure, Kubernetes, embeddings, scikit-learn, vector databases, automation workflows, model evaluation, ML lifecycle, Deployment, data preprocessing, LLMs, generative AI, retrieval-augmented generation, Monitoring, prompt engineering, RAG, AI agents