People Analytics, Data Scientist

Job Listing No: 9740000

People Analytics, Data Scientist

Position PurposeWant to work at the forefront of Artificial Intelligence, Analytics and Human Resources?   Cargill has a significant presence across most supply chains in the agricultural space. With that footprint comes massive amounts of data that can inform us about our people (employees, clients and customers) and ways to improve our value and impact to the business. As the Data Scientist, you will be part of the People Analytics team and will bring strong data science skills to our HR Analytics capabilities.  You will work in a growing Analytics team focused on: building up predictive insight to help leaders make better people decisions. You will work in a multidisciplinary team exploring, connecting, and mining internal data sources and will develop data models using algorithms for pattern detection and forecasting. In this position you will be managing projects that use advanced analytics techniques, such as optimization, computer vision, machine learning, and statistical analysis, to develop solutions that help deliver significant value to a variety of Cargill’s businesses and functions.Principal Accountabilities30% Advanced Data Analytics- Able to execute and articulate complex statistical and analytics techniques (e.g., machine learning, natural language processing, etc.) with stakeholder feedback. 20% Business Forecasting- Develop forecasting models by applying algorithms to large structured as well as unstructured data sets for our more complex projects.20% Problem Solving- Use analytics to solve key business questions and provide support for their aligned functions. throughout analytics process: proactive outreach on trends, incorporate external benchmarking. 20% Analytics Tools- Continuously seek out and refine new tools and vendor partnerships (e.g., Microsoft, Amazon, Anaplan, etc.) to create new capabilities for data analytics at Cargill to improve business decisions. 10% Data Modeling- Able to perform advanced modeling both high-level conceptual models to physical data models.  Skills: Required:  Ph.D. or MS in Computer Science from an accredited college/university in Computer Science, Statistics, Mathematics, Economics, Human Resources, Operations Research or related fields. Minimum  3  years relevant work experienceAble to understand various data structures and common methods in data transformationExperience in statistical modeling, machine learning, or data mining practiceDevelop custom data models and algorithms to apply to data setsKnowledge  in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacksKnowledge of   cloud-computing/web services: Redshift, S3, SparkExperience visualizing/presenting data for stakeholders using: Tableau or PowerBIStrong analytical and quantitative problem-solving abilityExcellent communication, relationship skills and a strong team player Preferred: Ph.D. from an accredited college/university in Business Analytics, Business Administration, Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or related fields.Experience in Human Resources  Experience working in a cloud environment e.g. Amazon Web Services.Strong presentation skills. Ability to present statistical results to lay persons in an easy to understand way. Experience using statistical computer languages (R, Python, SQL , etc.) to manipulate data and draw insights from large data setsUtilizing Statistical Natural Language Processing (NLP), lexical analysis and semantic processing Other relevant information to the position Career Development/Benefits: Organization Breadth – learning about multiple businesses and processes; fast paced, collaborative environment, ability to grow with data science team. Gained skills about the business as well as technical machine learning capabilities.    

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