Position: Senior Data Scientist (with GIS) (Spatially Health)
Company: SD Solutions
Category: Data Science
Experience required: 3y
English level: upper

On behalf of Spatially Health, SD Solutions is looking for a **Senior Data Scientist** that will help push the platform to the next level of analytic sophistication and usefulness.

We are seeking someone with extensive experience using **geospatial data & models** along with deep knowledge of a large variety of data mining/data analysis methods, **machine learning** and other advanced data analytic tools. The right candidate will have a passion for discovering solutions hidden in large data sets and working with a wide range of stakeholders and functional teams to improve business outcomes.

**Responsibilities:**

- Work with stakeholders (inside the organization as well as external customers) to identify opportunities for leveraging local and spatial data to drive business solutions.

- Mine and analyze data from US government and commercial databases to drive optimization and improvement of our customer’s product offerings, marketing techniques and business strategies.

- Build high-quality prediction systems that solve our customers' business problems by applying data mining and machine learning techniques; doing statistical analysis; and exploring, interpreting, and analyzing datasets for patterns of interest.

- Help build the company’s data science and machine learning capability: develop best practices and patterns for geospatial machine learning, develop reusable technical components, and identify and help establish needed technology stack and infrastructure.

**Requirements:**

- 4+ years of data science and machine learning experience with at least 4 years in working directly with clients or business users for delivering data science solutions to business problems.

- Programming knowledge & experience using mathematical & statistical programming languages (Python, Scala, Java, R) and ETL & data manipulation tools (Spark, Delta Lake, JupyterLab, PostgreSQL, SQL 
Server, etc.) to manipulate data and draw insights from very large data sets.

- Experience working with and creating data architectures and pipelines in the public clouds (Azure, AWS).

- Experience using and building models with machine/deep learning frameworks (Spark ML, Pandas, Numpy, Scikit-learn, TensorFlow, Keras, Pytorch) as well as cloud-vendor cognitive services.