Job description

  • Finance & Data Operations Data Science Team is tasked with delivering tangible value to business units through data-driven decision making.
  • This position is part of the Finance & Data Operations Data Science team delivering advanced analytics projects for different businesses. The individual will join a growing global data science organization spanning both on/offshore.
  • The incumbent is responsible for developing analytical models for projects collaborating with different business stakeholders & other partners and working across a range of technologies and tools.
  • The ideal candidate has a strong background in quantitative skills (like statistics, mathematics, advanced computing, machine learning) and has applied those skills in solving real-world problems across different businesses/functions.


Purpose

  • Develops analytics models using specialized tools based on the business problem and data available
  • Identifies the right set of models and develops the right code/package to execute them
  • Evaluates the validity of the model (both scientifically as well as from a business perspective)
  • Support the Data Science Team Lead in the design and execution of analytics projects
  • Work with stakeholders and subject matter experts to complete tasks and deliverables on the project


Requirements

Stakeholder Engagement Skills


  • Working collaboratively across multiple sets of stakeholders – business SMEs, IT, Data teams, Analytics resources, etc. to deliver on project deliverables and tasks
  • Identify actionable insights that directly address challenges/opportunities
  • Articulate business insights and recommendations (based on model output) to respective stakeholders
  • Understanding business KPI's, frameworks and drivers for performance
  • Proficiency Level: Skill


Industry / Functional Expertise

  • Provide deep business expertise preferably Oil & Gas - Upstream or Downstream businesses. (If these are not available, willing to consider other industries that are similar or related - manufacturing, mining, power generation, etc.)
  • Customer / Marketing – pricing analytics, churn prediction, cross-sell / up-sell, Market Basket Analysis, Product Recommendation, Marketing Mix Modeling, Campaign design, and effectiveness testing, Network Modeling, Customer segmentation, propensity analysis, customer lifetime value, profitability analysis, Customer experience (incl. voice of customer), CRM, Loyalty program management,
  • Supply Chain / Spend: Demand & Supply Forecasting, Spend Analytics, Vendor Scoring, Pricing analysis (buy-side), product substitution analysis, product portfolio optimization, Tail spend analysis, logistics/network/ route optimization, Contract Compliance
  • Proficiency Level: Skill


Modeling and Technology Skills

  • Deep expertise in machine learning techniques (supervised and unsupervised) statistics / mathematics / operations research including (but not limited to):
  • Advanced Machine learning techniques: Decision Trees, Neural Networks, Deep Learning, Support Vector Machines, Clustering, Bayesian Networks, Reinforcement Learning, Feature Reduction / engineering, Anomaly deduction, Natural Language Processing (incl. Theme deduction, sentiment analysis, Topic Modeling), Natural Language Generation
  • Statistics / Mathematics: Data Quality Analysis, Data identification, Hypothesis testing, Univariate / Multivariate Analysis, Cluster Analysis, Classification/PCA, Factor Analysis, Linear Modeling, Logit/Probit Model, Affinity & Association, Time Series, DoE, distribution/probability theory
  • Operations Research: Sensitivity Analysis – Shadow price, Allowable decrease or increase, Transportation problem & variants, Allocation Problem & variants, Selection problem, Multi-criteria decision-making, models, DEA, Employee Scheduling, Knapsack problem, Supply Chain Problem & variants, Location Selection, Network designing – VRP, TSP, Heuristics Modeling
  • Strong experience in specialized analytics tools and technologies (including, but not limited to)


  • SAS, Python, R, SPSS (preferably two out of 4)
  • Spotfire, Tableau, Qlickview
  • For Operations Research (AIMS, Cplex, Matlab)
  • Awareness of Data Bricks, Apache Spark, Hadoop
  • Awareness of Agile / Scrum ways of working


  • Identify the right modeling approach(es) for the given scenario and articulate why the approach fits
  • Assess data availability and modeling feasibility
  • Review the interpretation of models results
  • Evaluate model fit and based on business/function scenario