SCOTTSDALE, AZPosted 20 days ago

Job summary

• Job post source

  This job is directly from Affinaquest

• Job overview

  The Data Scientist role at Affinaquest involves advancing the Client Data Platform and analytics to empower fundraising and athletic operations through machine learning and data analysis.

• Responsibilities and impact

  The role requires developing and evolving machine learning models using Azure ML, Snowflake, dbt, and Airflow, collaborating with product teams, and delivering actionable insights to clients.

• Compensation and benefits

  The position offers a salary range of $150,000 to $160,000 with no travel required and a hybrid office policy at Scottsdale, AZ headquarters.

• Experience and skills

  Candidates must have a BS in data science or related fields, 5+ years in software development, 3+ years in SQL data manipulation, 2+ years in model validation, and experience with Python and machine learning frameworks; Azure ML and fundraising analytics experience are preferred.

• Work environment and culture

  Affinaquest promotes innovation, collaboration, and technical curiosity with a hybrid work environment fostering community engagement.

• Company information

  Affinaquest is a market-leading Salesforce ISV specializing in institutional advancement and collegiate athletics, serving top fundraising institutions and Power-5 athletic programs with advanced data analytics and CRM solutions.

• Team overview

  The Data Scientist will join the Product Engineering team, reporting to the Sr Manager of Product Engineering and working closely with Product Leaders and the Product Delivery Team.

• Job location and travel

  The role is based at Affinaquest's corporate headquarters in Scottsdale, AZ, with a hybrid office policy requiring presence three days a week.

• Unique job features

  The job features advanced use of Azure Machine Learning, MLOps practices, and evolving data science tools to innovate predictive and prescriptive analytics in fundraising and athletics.

Company overview

Affinaquest is a leading provider of cloud-based software solutions designed to help organizations manage and optimize their constituent relationships. The company primarily serves educational institutions, nonprofits, and sports organizations by offering a suite of products that facilitate data-driven engagement strategies, fundraising, and donor management. Affinaquest generates revenue through subscription-based services, providing clients with tools to enhance their operational efficiency and deepen their connections with supporters. Founded in 2010, Affinaquest has grown by acquiring complementary businesses and expanding its product offerings, establishing itself as a key player in the constituent relationship management (CRM) sector.

How to land this job

• Position your resume to highlight your expertise in Azure Machine Learning, Snowflake, dbt, and Airflow, emphasizing your ability to develop and deploy machine learning models within a product engineering environment at Affinaquest.

• Focus on showcasing your experience with Python data science libraries like Pandas, NumPy, and Scikit-learn, along with your skills in supervised and unsupervised learning, model validation, and MLOps practices, as these are key to the role.

• Apply through multiple channels including Affinaquest’s corporate careers page and LinkedIn to maximize your application’s visibility and ensure you reach the right hiring teams.

• Connect on LinkedIn with members of Affinaquest’s Product Engineering team or Data Science division; start conversations by mentioning your interest in their innovative use of Salesforce-native analytics or asking about their approach to integrating machine learning with fundraising and athletic operations data.

• Optimize your resume for ATS by incorporating keywords such as 'Azure Machine Learning,' 'Snowflake,' 'dbt,' 'Airflow,' 'MLOps,' 'predictive modeling,' and 'fundraising analytics' to pass automated screenings effectively.

• Use Jennie Johnson’s Power Apply feature to automate tailored applications across multiple platforms, identify relevant LinkedIn contacts for networking, and ensure your resume is optimized for ATS, freeing up your time to prepare for interviews and deepen your industry knowledge.