Sr. Data Scientist

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<div class="isg-job-description“>Who We Are

ServiceMac is a new sub-servicing entity backed by some of the leading practitioners of the mortgage servicing industry. ServiceMac is focused on providing superior technology, products, and services for the mortgage and real estate industries backed by highly personalized service and support. Through continuous innovation and acquisition, our offerings comprise of personalized solutions that span the mortgage continuum and enhance security, compliance, customer satisfaction, and profitability. Our inclusive, people-first culture has earned our company numerous accolades, including being named to the Fortune 100 Best Companies to Work For list for eleven consecutive years. We have also earned awards as a best place to work for women, diversity and LGBTQ+ employees, and have been included on more than 50 regional best places to work lists. First American will always strive to be a great place to work, for all.

What We Do

Identifies business trends and problems through complex data analysis. Interprets results from multiple sources using a variety of techniques, ranging from simple data aggregation via statistical analysis to complex data mining independently. Designs, develops, and implements the most valuable business solutions for the organization.

HOW YOU’LL CONTRIBUTE

• Leverage skills in handling very large datasets to perform exploratory analysis, construct relevant variables, and perform sample design
• Build and improve upon already built, complex predictive models from multiple sources to substantially improve business operations
• Automate feedback loops for algorithms/models in production and create repeatable processes and scalable data products
• Act as technical expert to recommend and develop innovative analytic solutions
• Communicate and present results to internal audiences, prepare presentations and documentation of models and analytical solutions
• Influence functional teams and develop best practices across the organization
• Other duties as assigned

WHAT YOU’LL BRING

Required education, experience, Certification/Licensure

• Bachelor’s degree or equivalent in a related quantitative field such as Mathematics, Statistics or Computer Science
• Master’s or PhD degree preferred
• 5-7 years of related work experience building predictive and descriptive statistical models
• Extensive experience with large datasets
• Knowledge and experience with diverse statistical and data mining methods (e.g. – GLM/Regression, Trees, Clustering, PCA, SVM, Time Series analysis etc.)
• Demonstrated proficiency with packages/languages such as SAS, R, or Python
• Understanding of RDBs and interactive SQL programming skills
• Aptitude for picking up new technologies
• Excellent written and verbal communication skills
• Excellent interpersonal, relationship-building and teamwork skills
• Self-motivated; self-starter
• Ability to manage multiple tasks, respond quickly to emergent problems, and focus both on long-range projects and immediate tasks
• Proficient in Microsoft Word, Excel and PowerPoint

What We Offer

By choice, we don’t simply accept individuality – we embrace it, we support it, and we thrive on it! Our People First Culture celebrates diversity, equity and inclusion not simply because it’s the right thing to do, but also because it’s the key to our success. We are proud to foster an authentic and inclusive workplace For All. You are free and encouraged to bring your entire, unique self to work. First American is an equal opportunity employer in every sense of the term.

Based on eligibility, First American offers a comprehensive benefits package including medical, dental, vision, 401k, PTO/paid sick leave and other great benefits like an employee stock purchase plan.
First American invests in its employees’ development and well-being, empowers them to provide superior customer service and encourages them to serve the communities where they live and work. First American is committed to diversity and inclusion. We are an equal opportunity employer.

 

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