Team Lead - Data Scientist Job in Shell

• Finance & Data Operations Data Science Team is tasked with delivering tangible value to business units within Shell through data-driven decision making.
• This position is part of Finance & Data Operations Data Science team leading a small team of data scientists delivering advanced analytics projects for different businesses within Shell. The individual will join a growing global data science organization spanning both on/offshore.
• Incumbent is responsible for leading and executing analytics projects in a business, collaborating with different business stakeholders and other partners, support the implementation of insights to realize tangible value for Shell, manage a team of data scientists (up to 5) and working across a range of technologies and tools.
• The ideal candidate has strong background in quantitative skills (like statistics, mathematics, advanced computing, machine learning), brings domain expertise, has applied those skills in solving real world problems across different businesses / functions and has managed small teams in delivering insights.

Purpose

• Lead the execution of analytics projects within the portfolio
• Design and articulate the data science solution relevant to the business problem / opportunity
• Lead identification of appropriate data science models and evaluate their fitment for the available data
• Articulate the insights from the models in business-friendly language and explain the workings of the model for business adoption

Skills

Stakeholder Management Skills

• Forming close relationships with business stakeholders across businesses / functions to comprehensively understand their areas of operation and apply those in project execution
• Clearly articulate the challenges / opportunities in business / function that can be supported by analytics
• Deliver actionable insights that directly address challenges / opportunities
• Guide articulation of business insights and recommendations (based on model output) based on understanding of business / function and respective stakeholders
• Understanding of business governance and control structures & selecting the right analytical approaches which are consistent with businesses control/governance framework

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.)

• Manufacturing / Industrial: Equipment Failure prediction, Maintenance Scheduling & Optimization, Inventory optimization, Cost Diagnostics, Energy Management
• 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
• Functional Analytics: Order-to-cash, Procure-to-Pay, Record-to-Report, Tax (Direct & Indirect), Financial Risk and Assurance (controls and governance), Master Data Management, Inter-group / Intra-group
• Trading & Risk Management: Across Credit & Market Risk - Value at Risk (VAR), Back testing, Stress testing

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
• • Risk: Simulation design and high-performance computing, GARCH modeling, Macro-economic / Market behaviour modeling
• • Process Analytics Process Discovery / Mining, BottleNeck analysis, Confirmation Testing, Process Benchmarking, Gap-to-Potential Assessment, SAP Data Models, SAP Table Structures (across SAP Modules 5-6 of the following: General Ledger Accounting, Accounts Payable, Accounts Receivable, Purchasing, Inventory Management, Material Planning, Invoice Verification, Material Requirement Planning (MRP), Warehouse Management, Vendor Valuation, Sales, Sales, Shipping and transportation, Billing or Invoice generation, Bills of Material (BOM), Sales Information system, Credit Control, Sales and production Planning, Demand Management, Material Requirement Planning, Capacity Requirement Planning

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

Project Management

• Execute end-to-end analytics projects - Project scoping, sourcing data, managing modeling, translating model results into business insights, and helping business partner understand insights and make decisions accordingly (help generate value for organization through analytics)
• Creating project management plan, running status update meetings, coordinating deliverables and timelines, and managing risks to project delivery
• Recognize the level of statistical knowledge in business stakeholders vs. analytics experts vs. IT resources and articulating how analytics will be applied appropriately
• Manage different moving parts - business stakeholders, IT, Analytics Resources, Data Experts, SMEs, etc. for the successful execution of the projects (executing multiple projects at a time will be considered a plus)