Position: Senior Data Scientist for Spatially Health
Company: SD Solutions
Category: Data Science
Experience required: 5y
English level: intermediate

**What are we doing?**

Spatially Health is a startup that uses complex and advanced spatial and socio-economic analytics, often referred to as Social Determinants of Health (SDoH). 
SDoH are factors that contribute to a person’s current state of health. These factors include genetics and biology, individual behavior, medical care, social circumstances, and physical environment. The platform provides insights and recommendations that enhance the equitable allocation of resources, improve patient engagement, lower cost outlays and ensure compliance with US government equity goals.

Spatially Health was founded to advance applied data science, location intelligence, and dignified healthcare. It is a data enrichment, analytics- and outcomes-focused technology company that helps organizations across the health industry use spatial intelligence to reveal needed connections and behavior patterns present in healthcare delivery. 

The geolocation app provides information about the population in a particular area and learns about the relevant vulnerabilities of medical governments to make health care and insurance easier and more effective for people.

**What will you do with us?**

- 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.

**What hard skills do we need?**

- 4+ years of data science and machine learning experience with at least 4 years of working directly with clients or business users to deliver 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.
- Extensive experience using geospatial data & models, along with deep knowledge of a large variety of data mining/analysis methods and other advanced data analytic tools.
- Passion for discovering solutions hidden in large data sets and working with a wide range of stakeholders and functional teams to improve business outcomes.

**The ideal candidate is adept a**t:

- Using large data sets to find opportunities for process optimization.
- Designing, implementing and using sophisticated algorithms and models to test the effectiveness of different courses of action.
- Driving business results with data-based insights.

**Frequently asked questions:**

- What language do we speak?
The team is international, so we use English to communicate. 
    
- Why was this position open?
It’s a new position opened in case of widening. 
    
- What is my career growth perspective on this position?
Your perspectives fully depend on your willingness. For example, you may become a Team Lead. 
    
- How old is the company?
The company was founded in 2019. 
    
- Where are the people who taking decisions located?
In the USA. 
    
- What is the product stage now?
The company has a product that will be improved.