Bengaluru, Bangalore North, Bangalore Urban, Karnataka, India
Member Since 2020
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Sumit Singh

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    About the Candidate

    1. Senior Data Scientist, Remote Job,Nestle, Switzerland. Aug 2019 – till date
    LSTM, VAR & VECM implementation for Price Prediction of commodities futures and energy prices.
    GBM & design of ensemble for selection from different models.
    Impact: The prediction beats the benchmark 70%+ times as shown by back testing.
    Coding and implementation of Mean Reversion for Price Risk Management for commodities futures in
    Python. Implementing it for energy as well.
    Impact: Back Testing shows savings around 100m CHF per year.

    2. Senior Data Scientist, Telecom Analytics, Subex, Bangalore. Jun 2017- Jul 2019
    Worked as a senior data scientist to create statistical modeling with a team. Main projects: Anomaly Analytics and Social Network Analytics using large scale telecom data. Used algorithms/ methods such as PCA, LSTM with Time Series to detect anomaly and minimize false positives.
    Worked on diffusion modelling to create ideas for Social/Cell network analysis using networkx, pandas package in Python.
    Project management work such as planning KRAs. Designing and conducting training on Statistical Learning, R and Python.

    3. Manager, Fin Enterprise Analytics Philips Innovation Campus, Bangalore Jan2016-May 2017
    Projects: Working Capital Analytics, Factory P&L Analysis. Methods Used: ML(Decision Tree, Random Forest,
    PCA, Cluster Analysis), ARIMA. Impact – 10% reduction in locked working capital. Languages: SQl, R, Python.

     

    4. PhD Paper3

    Inventory Control of Perishable Inventory which can be converted into different forms
    Desc: Multi-Period decision problem for convertible perishable products with stochastic demand (with different statistical distributions) and price in different periods. Techniques Used: Convex Optimization, News-vendor Model, Differential Calculus, Sensitivity analysis, Simulation in Python.

    5. PhD Paper 1 and 2
    Inventory Control of Two Stage Perishable Inventory
    Desc: Stochastic Deterministic model for two stage perishable inventory (used in Airline, Auto mobile and PCB industry) control.
    Techniques used: EOQ Model, discreet time Markov chain, Queuing Theory, Alternating Renewal Process and various statistical distributions.
    Result: Closed form/Algorithmic solution to determine lot size, unit size and N policy of queuing.
    Techniques Explored: Markov Decision Process, Multi-armed Bandit models, Dynamic Programming, Stochastic Programming, Other Machine Learning Algorithms.

    Education
    2008-2018
    Indian Institute of Management Bangalore PhD

    PhD in Decision Science. Topic: Stochastic Modeling of two-stage perishable inventory.

    200-2004
    Indian Institute of Technology (ISM) Dhanbad B.Tech (Mineral Engineering)

    Bachelor of Technology in Mineral Engineering

    Skills
    Deep LearningForecastingMachine LearningPythonQuantitative ModelingSQLStatistics
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