Data Scientist

Collaborates with business and analytics leaders to generate insights and answer business questions by using analytics techniques such as advanced data visualization, statistical analysis, randomized testing, predictive modeling, forecasting, optimization, and/or machine learning.

Proposes innovative ways to look at problems by using these approaches on available enterprise data as well as customer third party data and information.

Validates findings using experimental and iterative approaches.

This level performs basic statistical analysis of low to moderately complex data from a single source, with heavy reliance on industry/standardized tools and existing models.

Output is reviewed by higher-level Data Scientists or Analytics Manager for execution.

• Works with data sets and performs appropriate analytical methodology to provide insights and decision modeling for the business.
• Creates and implements algorithms which manage the data to enable it to be analyzed in an efficient manner.
• Supports the communication of derived insights, especially through appropriate visualization techniques.
• Supports the identification of the required data sources and works with Data Wranglers and/or IT to implement methodologies to retrieve and use this data.
• Stays abreast of the latest appropriate analytical techniques and recommends these where needed. Explains implementation and usage in business terms.
• Applies analytical techniques to prospect for business insights and find patterns in data which could be valuable for the business

• Quantitative analytical skills
• Knowledge of appropriate industry
• Good interpersonal, negotiation and conflict resolution skills.
• Excellence in verbal and written communication forms with emphasis on persuasive communication, tact and negotiation.
• Business process knowledge of assigned area(s) and/or function(s).
• Knowledge of advanced data gathering and analysis techniques, including statistical analysis.

• Degree in a Math discipline or equivalent experience.
  
  - University Degree (4 years or equivalent) Economics - University Degree (4 years or equivalent)
• Statistics - University Degree (4 years or equivalent)

• Internal or external industry specific experience in relevant discipline. (1 - 3 years)
• Data analytics experience. (1 - 3 years)
• Background or proven experience in mining data for analytics insights. (1 - 3 years)
• Good exposure to enterprise statistical tools like SAS, Statistica, SPSS or SAS E Miner (1 - 3 years)

• Communicate with impact your findings and methodologies to stakeholders with a variety of backgrounds.
• Work with high resolution machine and agronomic data in the development and testing of predictive models.
• Develop and deliver production-ready machine learning approaches to yield insights and recommendations from precision agriculture data.
• Define, quantify, and analyze Key Performance Indicators that define successful customer outcomes.
• Work closely with the Data Engineering teams to ensure data is stored efficiently and can support the required analytics.
• Demonstrated competency in developing production-ready models in an Object‑Oriented Prog language such as Python.
• Demonstrated competency in using data‑access technologies such as SQL, Spark, Databricks, etc.
• Experience with Visualization tools such as Tableau, Kepler.gl, etc.
• Experience with Data Modeling techniques such as Normalization, data quality and coverage assessment, attribute analysis, performance management, etc.
• Experience building machine learning models such as Regression, supervised learning, unsupervised learning, probabilistic inference, natural language modeling, etc.
• Excellent communication skills. Able to effectively lead meetings, to document work for reproduction, to write persuasively, to communicate proof‑of‑concepts, and to effectively take notes.

• Experience with Geospatial data search and analysis, geo‑indexing techniques, vector and raster data structures.
• Experience with remote sensing, GIS tools, and satellite imagery analysis.
• Experience with CVML
• Experience with advanced AI techniques and tools.
• Examples of professional work such as publications, patents, a portfolio of relevant project‑work, etc.
• Familiarity with Distributed Datasets
• Experienced with a variety of data structures such as time‑series, geo‑tagged, text, structured, and unstructured.
• Experience with simulations such as Monte Carlo simulation, Gibbs sampling, etc.
• Experience with model validation, measuring model bias, measuring model drift, etc.
• Experience collaborating with stakeholders from disciplines such as Product, Sales, Finance, etc.
• Ability to communicate complex analytical insights in a manner which is clearly understandable by nontechnical audiences.