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.
