Delivery Lead at Randstad
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Data Scientist - Manufacturing/Auto/Infra/Oil & Gas Sector (8-16 yrs)
- Lead the execution of analytics projects within the portfolio
- Design and articulate the data science solution relevant to the business problem/opportunity
- Lead the 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
- Provide support to the business value manager in managing the portfolio
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 and/or.
- 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 and/or.
- 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 and/or.
- Risk: Simulation design and high-performance computing, GARCH modeling, Macro-economic / Market behavior modeling and/or.
Strong experience in specialized analytics tools and technologies (including, but not limited to):
- SAS, Python, R, SPSS (preferably 2 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 interpretation of models results
- Evaluate model fit and based on business/function scenario
Proficiency Level: Mastery