Lead Data Scientist

At Capgemini Invent, we believe difference drives change. As inventive transformation consultants, we blend our strategic, creative and scientific capabilities,collaborating closely with clients to deliver cutting-edge solutions. Join us to drive transformation tailored to our client''s challenges of today and tomorrow.Informed and validated by science and data. Superpowered by creativity and design. All underpinned by technology created with purpose.

Your Role

• Programming Languages Python NumPy, SciPy, Pandas, MatPlotLib, Seaborne Databases RDBMS (MySQL, Oracle etc.), NoSQL Stores (HBase, Cassandra etc.) ML/DL Frameworks SciKitLearn, TensorFlow (Keras), PyTorch, Big data ML Frameworks - Spark (Spark-ML, Graph-X), H2O. Cloud Azure/AWS/GCP.
• Predictive and Prescriptive modelling using Statistical and Machine Learning algorithms including but not limited to Time Series, Regression, Trees, Ensembles, Neural-Nets (Deep Shallow CNN, LSTM, Transformers etc.). Experience with open-source OCR engines like Tesseract, Speech recognition, Computer Vision, face recognition, emotion detection etc. is a plus.
• Unsupervised learning Market Basket Analysis, Collaborative Filtering, Dimensionality Reduction, good understanding of common matrix decomposition approaches like SVD. Various Clustering approaches Hierarchical, Centroid-based, Density-based, Distribution-based, Graph-based clustering like Spectral.
• NLP Information Extraction, Similarity Matching, Sentiment Analysis, Text Clustering, Semantic Analysis, Document Summarization, Context Mapping/Understanding, Intent Classification, Word Embeddings, Vector Space Models, experience with libraries like NLTK, Spacy, Stanford Core-NLP is a plus. Usage of Transformers for NLP and experience with LLMs like (ChatGPT, Llama) and usage of RAGs (vector stores like LangChain LangGraps), building Agentic AI applications.

Your Profile

Graph Analytics Familiarity with Graph Algorithms (Directed Undirected) Traversal (BFS, DFS), Cycle Detection (Bellman Ford, Flyod Warshall), Shortest Path (Dijkstra, A*) etc. Building Knowledge Graphs with unstructured data and knowledge graph optimizations like PageRank/TrustRank is expected

Mathematical Optimization Familiarity with common optimization algorithms, both discreteLinear, Mixed-Integer, Goal, Dynamic etc and continuous GD and its variants, Newtons method etc. is expected. Experience with Simulated Annealing and exposure to ML inspired evolutionary optimization algorithms like Genetic Algorithm Genetic Programming for optimization is a plus.

Simulations Monte Carlo Simulation, Discrete-Event Simulation, Agent-Based Simulation, Hybrid Simulation, System Dynamics, Genetic Algorithm based Simulation.

Model Deployment ML pipeline formation, data security and scrutiny check and ML-Ops for productionizing a built model on-premises and on cloud.

What you will love about working here

• We recognize the significance of flexible work arrangements to provide support. Be it remote work, or flexible work hours, you will get an environment to maintain healthy work life balance.
• At the heart of our mission is your career growth. Our array of career growth programs and diverse professions are crafted to support you in exploring a world of opportunities.
• Equip yourself with valuable certifications in the latest technologies such as Generative AI.