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Dr. Dhiraj Sharma

Dr. Dhiraj Sharma

Principal of Artificial Intelligence and Data Science Delivery at Capgemini Invent, North America

Warren, New Jersey

Strategic thinker, creative problem solver, technical leader, project manager, and a strong hands-on data scientist with 15+ years of experience in AI, data science, machine learning, statistics, large databases, distributed-fault-tolerant computing, and large digitization projects. Possesses a wide range of problem solving, interpersonal, technical, and communication skills. Pays attention to details, excellent team player, and goal oriented. Driven by results and business impact.

Machine Learning:
• Clustering, classification – SVM, KNN, logistic, Bayesian, decision trees (random, boosted), model explanation (LIME).
• Artificial neural networks – auto-coders, word embedding, CNN – Keras, Tensor Flow.
• Dimensionality reduction – SVD, NMF, Self-Organizing Maps, identification of latent attributes.
• KNN techniques and vector space similarity modeling – for recommendation engines and product affinity.

Text Mining:
• NLP: bag-of-words, topic analysis (LSA, pLSA, LDA), word embedding (GloVe, Word2Vec, Gensim), distance metrics, SOM to create “near-equivalent” words, Explicit Semantic Analysis.
• Document similarity analysis and clustering: data-driven determination of number of clusters, extraction of skill/talent profile from resumes, extraction of culture profiles from annual performance review text, matching documents to customer requests, intent/topic detection in forum text, and spam detection.

Statistical Analysis:
• Ordinary and logistic regression models, GLM. Piecewise and localized regression.
• PCA, factor analysis, kernel smoothing.
• Variable selection, hypothesis testing, inferencing.

Software Engineering:
• Large databases, data warehousing,
• Data integration, preprocessing, and harmonization.
• Mathematical algorithms.
• Distributed software (Hadoop, map-reduce). High-performance, real-time software.

Digital Transformation:
• Define digital strategies to support data-driven decision making.
• Create advanced analytics, data science, and AI facilities to achieve transformational objectives.
• Identify gaps in data landscape that would hinder achieving digitization objectives.
• Business sectors: manufacturing, consumer goods, telecommunications, retail, and financial services.


  • Lead Data Scientist (PDS Paragon Development Systems)
  • Sr. Director, Master Data Scientist - Retention and Acquisition (Cognizant Technology Solutions)
  • Team Lead - People Analytics (Bloomberg LP)
  • Principal of Artificial Intelligence and Data Science Delivery (Capgemini Invent)
  • Director and Lead Data Scientist, People Analytics Function (BlackRock)


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