Lead Data Scientist

Smith Arnold Partners; Scottsdale (AZ); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Smith Arnold Partners is seeking a Lead Data Scientist in Scottsdale, AZ (Hybrid) to drive analytics initiatives from problem framing to measurable outcomes. You will partner with executives to identify opportunities and deliver data-driven decisions that influence revenue and operations.

You will develop predictive models, mentor junior researchers, and present insights to senior leaders, ensuring clear storytelling and strategic impact across functions.

Qualifications

• 10+ years in Data Science, Predictive Analytics, or related fields.
• 3+ years building ML models with Python.
• Experience translating insights to executive audiences.
• Strong SQL and data wrangling skills.

Responsibilities

• Lead analytics initiatives from problem definition through implementation and measurement.
• Translate technical results into clear business recommendations for executives.
• Mentor junior data scientists and elevate analytics practices.

Skills

Python

SQL

Statistical Methods

Executive comms

Project management

Mentoring

Education

Bachelor's in quantitative field

Tools

Snowflake

AWS S3

XGBoost

CatBoost

Random Forest

Job description

Imagine joining an organization where data is not just used to report on the business, but to actively shape its future. This high-growth, technology-driven company manages a large national portfolio of residential assets and relies heavily on advanced analytics to drive smarter decisions, improve customer experiences, optimize operations, and create measurable business value. The leadership team embraces innovation, encourages curiosity, and empowers employees to challenge conventional thinking. Here, data scientists are viewed as strategic business partners, giving you a seat at the table with senior executives while providing the autonomy to solve meaningful, business-critical problems.

This is an ideal role for an experienced Data Scientist who wants more than model development. You'll be empowered to identify business opportunities, influence strategic decisions, and drive measurable outcomes across a growing organization. If you're equally comfortable building machine learning solutions and presenting recommendations to executive leadership, this role offers the visibility, ownership, and impact that define a truly exceptional career opportunity.

Why This Should Be Your Next Employer

• Your work will directly influence revenue, customer experience, operational efficiency, and strategic decision-making across the organization.
• Collaborate closely with Directors, Vice Presidents, and C-suite executives on high-priority initiatives that drive business performance.
• From identifying opportunities to building predictive models and implementing recommendations, you'll have meaningful ownership and autonomy.

What Employees Are Saying

• "My work doesn't sit on a shelf. The models and insights I build directly influence major business decisions and operational strategies."
• "Leadership genuinely values analytical thinking. I've had opportunities to present recommendations to executives and see my ideas implemented."
• "The challenges are complex, the data is rich, and the impact is real. It's an environment where you continuously learn and grow."

Title: Lead Data Scientist

Location: Scottsdale, AZ (Hybrid)

Responsibilities

• Lead complex analytics initiatives from problem definition through implementation and measurement.
• Partner with business leaders to identify opportunities and solve critical operational and strategic challenges.
• Develop predictive models and machine learning solutions using large, complex datasets.
• Create data-driven solutions related to customer retention, risk assessment, operational forecasting, and cost optimization.
• Apply advanced analytical methods including supervised and unsupervised machine learning, regression analysis, forecasting, clustering, and classification techniques.
• Gather, clean, validate, and integrate data from multiple internal and external sources.
• Conduct statistical analysis, experimentation, back-testing, and quantitative research to validate findings.
• Translate technical results into clear, compelling business recommendations for executive audiences.
• Design dashboards, presentations, and analytical storytelling materials that influence decision-making.
• Identify process improvement opportunities that reduce risk and improve operational performance.
• Mentor and develop junior data scientists while helping elevate analytical best practices across the team.
• Collaborate cross-functionally with operations, finance, technology, and executive leadership teams.

Qualifications

• 10+ years of experience in Data Science, Predictive Analytics, Advanced Analytics, Quantitative Research, Risk Modeling, or a related discipline.
• Bachelor's degree in Statistics, Mathematics, Engineering, Economics, Computer Science, or another quantitative field.
• 3+ years of hands-on experience developing machine learning models using Python.
• Advanced SQL skills with experience querying and optimizing large datasets.
• Strong foundation in statistics, predictive modeling, probability, sampling techniques, and experimental design.
• Experience communicating analytical insights and recommendations to executive stakeholders.
• Ability to translate complex technical findings into practical business actions.
• Strong project management and prioritization skills with the ability to manage multiple initiatives simultaneously.
• Proven ability to build credibility and influence with technical and non-technical stakeholders.

Preferred

• Experience with Snowflake and modern cloud-based data platforms.
• Experience with AWS environments, including S3 and related services.
• Background in risk analytics, portfolio management, credit scoring, or predictive risk modeling.
• Experience working in highly regulated industries where model governance, validation, and audit requirements are important.
• Familiarity with model risk management frameworks such as SR 11-7.
• Experience leveraging algorithms such as XGBoost, CatBoost, Random Forest, Decision Trees, KNN, clustering methods, regression, and time-series forecasting.