Lead Data Scientist

Your Role

• Programming Languages - Python - NumPy, SciPy, Pandas, MatPlotLib, SeaborneDatabases - 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 discrete- Linear, Mixed-Integer, Goal, Dynamic etc and continuous - GD and its variants, Newton's 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.