[Hiring] Lead Data Scientist @Capgemini

Role Description

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.

Qualifications

• 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 is a plus.
• Unsupervised Learning:
  • Market Basket Analysis
  • Collaborative Filtering
  • Dimensionality Reduction
  • 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 & LangGraphs), building Agentic AI applications.

Requirements

• Graph Analytics:
  • Familiarity with Graph Algorithms (Directed & Undirected) – Traversal (BFS, DFS), Cycle Detection (Bellman Ford, Floyd 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 discrete (Linear, Mixed-Integer, Goal, Dynamic) and continuous (GD and its variants, Newton’s method) 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
  • ML-Ops for productionizing a built model on-premises and on cloud

Benefits

• Flexible work arrangements to provide support, including remote work and flexible work hours.
• Career growth programs and diverse professions crafted to support exploration of opportunities.
• Valuable certifications in the latest technologies such as Generative AI.