Data Science Professional Professional @ Capgemini

• Drive data analytics strategy from design through implementation while acting as a trusted advisor to our clients. Develop analytics roadmap and business case; analytics and data strategy; business analysis for problem solving; client engagement; stakeholder management; drive end-to-end analytics project
• Data and Analytics Solution Architecture - helping our clients define, design and implement complex data solutions for critical business priorities
• Leading projects which consist of delivery consultants with different analytics division like big data, data science, data engineering, data warehousing and data visualization to solve business problem
• Managing client expectations and engagement staffing, issues and risks.

• Education :Engineering graduates (B.E/B.Tech / PG degree (M.E/ M.Tech/ M.Sc/ MBA etc.) Other graduates (Mathematics / Statistics / Computer Science Applications). 4-8 years of experience in delivering data analytics delivery engagements in one/two of the following areas preferably Consumer Products, Retail, Manufacturing, Automotive, Life Sciences, Financial Services, Telecom, Media, Technology.
• Exposure of analytics frameworks, projects lifecycle. Hands-on experience of data-science and machine learning techniques algorithms for supervised unsupervised problems, NLP and computer vision. Applied statistics skills, such as distributions, statistical inference testing, etc.
• Excellent understanding and hand-on experience on building Deep-learning models for text and image analytics (such as ANNs, CNNs, LSTM, Transfer Learning, Encoder and decoder, etc). Good understanding of GenAI Agentic AI. Proficient in coding in common data science language tools such as R, Python, Go, SAS, Matlab etc. Experience with common data science toolkits, such as NumPy, Pandas, StatModel, Scikit learn, SciPy, NLTK, Spacy, Gensim, OpenCV etc. Experience with common data science frameworks such as Tensorflow, Keras, PyTorch, XGBoost, H2O etc.
• Exposure or knowledge in cloud (AWS/GCP/Azure) and big data technologies such as Hadoop, Hive, MapReduce, Pyspark etc. Experience on deployment of model in production, MLOps, GenAI technologies, project delivery management team handling and support in RFP submission and client pitches from AI/ ML perspective.

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