Shell looking for Data Analyst
Experience Needed: 1+ Year
General Position Definition:
The 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 a good background in quantitative skills (like statistics, mathematics, advanced computing) and has applied those skills in solving real-world problems
Support the Data Analytics team in the design and execution of analytics projects
Work with data and technology experts to help execute analytics projects and deploy solutions
Deep expertise in machine learning techniques (supervised and unsupervised) statistics/mathematics / operations research including (but not limited to):
Advanced Machine learning techniques (Mandatory): 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 (Mandatory) 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 (Good to have; Added Advantage): 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 (Good to have; Added Advantage): Simulation design and high-performance computing, GARCH modeling, Macro-economic / Market behavior modeling
Strong experience in specialized analytics tools and technologies (including, but not limited to)
SAS, Python, R, Alteryx, SQL (preferably two out of 5)
Spotfire, Tableau, QlikView, Power BI (preferably 1 out of 4)
Identify the right modeling approach(es) for the given scenario
Assess data availability and modeling feasibility
Review interpretation of models results
1+ Year/ Fresher with Advanced university degree in Mathematics, Statistics, Engineering, Economics, Quantitative Finance, OR, etc.
Good communication skills
Eagerness to learn and ability to work under tight deadlines
Reference ID: R27409
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