Data Scientist

Today’s logistics marketplace is an ever-changing landscape where you can make your mark. Spot gives you the tools to tackle industry challenges for our partners. Here, initiative, drive, and teamwork form the basis for a rewarding, fast-paced career.

About The Role:

We are seeking a talented Data Scientist who has a passion for solving business problems by providing critical & actionable insights through data modeling and analysis. You’ll partner directly with the most senior leadership at Spot, along with our data/technology teams to understand and solve complex business problems. You will be using our rich database of millions of data points to build a proprietary & dynamic pricing model, as well as other operational models that will greatly impact our growth, profitability & overall efficiency.

You would be a great fit for our team if you’re a self-starter who is passionate about exploring the intersection of real business problems with huge data sets and finding ways to deliver value to our daily operations. This person will be a game changer in an industry that is ripe for AI/data analytics.

Responsibilities:

• Use data modeling & analytical approaches to develop proprietary, dynamic pricing models for daily operational use.
• Apply data science techniques including machine learning, AI, and statistical modeling across multiple functional areas of the organization to drive improved decision making.
• Improve existing methodologies and tools by incorporating new data sources and implementing model enhancements.
• Serve as a highly qualified technical resource to Spot’s senior management team, with heavy involvement in driving continued growth & scaling of the organization.
• Create and track accuracy and performance metrics (both technical and business metrics) of models & tools.
• Create, enhance, and maintain technical documentation, and present it to her scientists, engineers, and business leaders.
  
  

Qualifications:

• 2+ years of experience with data scripting languages (e.g., SQL, Python, R etc.,) or statistical/mathematical software (e.g., R, SAS, or MATLAB).
• At least 2 years of experience and understanding of Microsoft Azure Synapse ML Tool.
• API development experience.
• At least 2-3 years of experience in quantitative analytics or data modeling.
• Deep understanding of predictive modeling, machine learning, clustering and classification techniques, and algorithms.
• Fluency in a programming language (Python, C, C++, Java, SQL).
• Familiarity with Big Data frameworks and visualization tools (Cassandra, Hadoop, Spark, Tableau, Power BI).
  
  

Additional Information:

• Visa sponsorship is not available for this position.
• This position is available at our Indianapolis, IN or Tampa, FL office locations only
  
  

Spot is built on relationships, combining 24/7 support with a proven, passionate, and dedicated team of logistics professionals. You can reach your true potential through the unlimited opportunities we offer. When you put in the effort, show initiative, solve problems, and build lasting client relationships, you earn respect and rewards. You’ll also be a key component to the success of an industry leader. At Spot, we’ve never lost the entrepreneurial spirit that provides the foundation for our success.

Spot Freight, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. Spot Freight is committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a disability, you need a reasonable accommodation, please contact our Human Resources team to notify us of your request. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.