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Build and deploy scalable data science algorithms by applying statistical analytics, Machine Learning (ML), Anomaly detection, and other techniques to enable supply planning. Knowledge of common tools for data storage and processing (e.g. Spark, Hadoop Map/Reduce, Hive, Cassandra) including drilling into problems of running large scale software in a big network.
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Experience with big data technologies (e.g., Hadoop, Spark, Databricks, Snowflake) and cloud platforms (e.g., AWS, Azure). Strong proficiency in statistical analysis and modeling techniques, such as linear regression, logistic regression, decision trees, random forests, clustering, deep learning algorithms, boosting, text mining and NLP.
Full-timeExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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Experience in NLP, computer vision, deep learning techniques. Collaborate with cross-functional teams including data science, software engineering, and product management to build predictive models, forecasting tools, and recommendation systems.
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PhD in Computer Science, Operations Research, Statistics, or highly quantitative field (or equivalent experience) with strength in Deep Learning, Machine Learning, Data Mining, Statistical or other mathematical analysis.
$183,400 - $238,500 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.
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Exposure to cloud platforms and big data systems such as Hadoop HDFS, Hive is a plus. Work on the latest applications of data science to solve business problems. 7-10 years of professional work experience with at least 5 years in data analytics and strong third-party data experience - Nielsen, IRI (now Circana), Walmart Luminate, Kroger etc.
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To this end, the team employs methods from Natural Language Processing, Deep learning, multi-armed bandits and reinforcement learning, Bayesian Optimization, causal and statistical inference, and econometrics to drive discovery across the customer journey.
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Ability to work with IT and Data Engineering teams to help embed analytic outputs in business processes. Solid understanding of statistical and machine learning algorithms. Develop customer understanding through customer data, contributing to the overall analysis of the consumer path to purchase.
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We are looking for someone with a good blend of business consulting skills and a data analytics background. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data.
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Strong SQL skills and hands-on experience with analytic tools like R & Python & visualization tools like Qlik or Tableau. Ability to engage with executive/VP level stakeholders from the clients team to translate business problems into high-level analytics solution approach.
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Tiger Analytics is an advanced analytics consulting firm. Graduate in Business Analytics or MBA or equivalent work experience. Work directly with client stakeholders to translate business problems into high-level analytics solution designs.
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Strong project management and team management skills and ability to work with global teams. Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.
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Participate in discussions with team members to select and apply relevant analytic techniques and create actionable business insights. Responsible for managing analytics projects, collaborating with client stakeholders and Tigers team situated globally.
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Present analytic solutions to business audiences highlighting the robustness of the solution and how it could help generate business value. Responsible for making presentations to senior management, communicating results to business teams, and developing plans to help operationalize analytic solution.
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Strong background in two or more of the following areas: machine learning/AI algorithms, computations/statistical learning theory, scalable systems (e.g. Spark, Hadoop), large scale data analysis, optimization, functional analysis and deep learning.
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