Your role
- 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.
- 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.
Your Profile
- 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
- Experienced in Agile way of working, manage team effort and track through JIRA
- Experience in Proposal, RFP, RFQ and pitch creations and delivery to the big forum.
- Experience in POC, MVP, PoV and assets creations with innovative use cases
- Experience working in a consulting environment is highly desirable.