Shell Careers Recruitment 2023– India hiring for freshers as Data Science Analyst of Bachelors or Masters to work at Bengaluru or Chennai location. The detailed eligibility criteria and application process are given below.
Job Summary
Details About Shell Recruitment 2023:
Company Website : www.shell.in
Wikipedia: Shell
Job Role: Data Science Analyst
Qualification: Bachelors or Masters
Batch: Recent Batches
Experience: Freshers
Salary: INR 15.5 LPA (Expected)
Job Location: Bangaalore/ Chennai
Last Date: ASAP
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General Position Definition of Shell Recruitment:
- 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 team of data scientists delivering advanced analytics projects for different businesses within Shell. The individual will join a growing global data science team spanning both on/offshore.
- Incumbent is responsible for supporting the development of analytical models for projects collaborating with different business stakeholders & other partners and working across a range of technologies and tools.
- The ideal candidate has good background in quantitative skills (like statistics, mathematics, advanced computing) and has applied those skills in solving real world problems
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Purpose
- Support the Data Analytics team in design and execution of analytics projects
- Work with data and technology experts to help execute analytics projects and deploy solutions
Accountabilities
- Developing optimal analytical models that address the business requirement, with acceptable levels of accuracy
- Writing efficient code that incorporates best practices in that technology
Skills for Shell Careers Hiring:
Industry / Functional Expertise
- Exposure to any business domain during academic internship/other roles before is desirable –
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, 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
- Proficiency Level: Knowledge
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Modeling and Technology Skills
- Knowledge and hands on experience in any of the following 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 behavior 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
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Typically, each role will look at one of two of the above skills – not all of them
- Trained in usage of any of the specialized analytics tools and technologies (including, but not limited to)
- SAS, Python, R, Databricks (atleast one)
- Spotfire, Tableau, Qlikview
- For Operations Research (AIMS, Cplex, Matlab)
- Awareness of working with Cloud (Azure or AWS)
- Awareness of Agile / Scrum ways of working
- Review interpretation of models results
- Evaluate model fit and based on business / function scenario
- Proficiency Level: Knowledge
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Special Challenges
- Rapid onboarding on projects, understanding analytics goal and working with ill-defined datasets
- Communicating technical jargon in plain English to colleagues within Data Analytics and outside
- Virtual working with network of colleagues located throughout the globe
Dimensions
- Support design and delivery of analytics projects, within or cutting across upstream and downstream business units in Shell
Experience
- Freshers from universities with internship experience / Candidates from other fields of work with no working experience of Data Science (just trained in data science)
- Advanced university degree in Mathematics, Statistics, Engineering, Economics, Quantitative Finance, OR, etc.
- Good interpersonal communication skills
- Eagerness to learn and ability to work under tight deadlines
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How to Apply For Shell Careers Recruitment 2023?
All interested and eligible candidates can apply for Shell Off Campus Drive online by the following link as soon as possible.
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