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CIEL/SEL/12534: AI Lead at CIEL HR Services

Responsibilities

This position is to lead the Data Science team delivering advanced analytics projects for different verticals and Brands. The individual will Lead the data science team and will be responsible for developing analytical models for projects collaborating with different business stakeholders & other partners, working across a range of technologies and tools.

The ideal candidate has a strong background in quantitative skills (like statistics, mathematics, advanced computing, machine learning) and has applied those skills in solving real-world problems across different businesses/functions.

The ideal candidate should have the ability to design the solution, including data extraction, data modelling, transformation, integration with downstream systems.

End-to-end hands-on ownership of machine learning features and various projects

Exceptional interpersonal and communication skills, including the ability to describe the logic and implications of a complex model to all types of partners (product managers, engineers and senior executive

Modeling and Technology Skills

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Deep expertise in machine learning techniques (supervised and unsupervised) statistics/mathematics/ operations research including (but not limited to)

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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) and / or 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, Ensemble techniques, 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, SalesMan Scheduling, Route Optimization, Supply Chain Problem & variants, Network designing

Strong experience in specialized analytics tools and technologies (including, but not limited to)



Python and/or R

Proficient with popular ML frameworks such as Keras, Caffe, Tensorflow, CNTK, CUDA

Understanding or desire to learn end to end Machine Learning technology stack (Tools such as Kubeflow, Kubernetes, SeldonCore, H2O, Data Robot, Anaconda, OpenScale etc)

Power BI, Spotfire, Tableau, Qlickview

Optimization techniques for Factory Operations

Awareness of Data Bricks, Apache Spark, Hadoop

Awareness of Agile / Scrum ways of working

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

Assess data availability and modeling feasibility

Review the interpretation of models results

Evaluate model fit and based on business/function scenario

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by Tsutomu Narushima