Job Description

Job Description -

  • 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 -

  1. Lead the execution of analytics projects within the portfolio
  2. Design and articulate the data science solution relevant to the business problem / opportunity
  3. Lead identification of appropriate data science models and evaluate their fitment for the available data
  4. Articulate the insights from the models in business-friendly language and explain the workings of the model for business adoption
  5. Provide support to the business value manager in managing the portfolio

Requirements -

SKILL -STAKEHOLDER MANAGEMENT SKILL -

  • 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
  • Understanding business KPI's, frameworks and drivers for performance

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.)
  • functional expertise in any one or more of the following industry / functional areas
  • 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, Marketing Mix Modeling, Campaign design and effectiveness testing, Network Modeling, Customer segmentation, propensity analysis, customer lifetime value, profitability analysis
  • 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
  • Proficiency Level: Skill-to-Mastery

Modeling and Technology Skills -

  1. Advanced machine learning techniques (supervised and unsupervised) including (but not limited to):
  • Decision Trees, Neural Networks, Deep Learning, Support Vector Machines, Clustering, Bayesian Networks, Reinforcement Learning, Feature Reduction / engineering, Anomaly deduction
  • Natural Language Processing, Natural Language Generation

2. Statistics / mathematics / operations research including (but not limited to):

  • 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

3. Specialized analytics tools and technologies (including, but not limited to)

  • SAS, Python, R, SPSS
  • For OR (AIMS, Cplex, Matlab)
  • Spotfire, Tableau, Qlickview
  • Awareness of Data Bricks, Apache Spark, Hadoop
  • Awareness of Agile / Scrum ways of working

4. Identify the right modeling approach(es) for given scenario and articulate why the approach fits

5. Assess data availability and modeling feasibility

6. Review interpretation of models results

7. Evaluate model fit and based on business / function scenario

8. Proficiency Level: Mastery

Job Type
  • Full Time
  • Full Time

Functional Area

Software Development

Industry

Oil/Gas/Power/Energy

Education Required

Not specified

Experience Required

6-10 years

Skills Required

Data Science