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Data Scientist Director

CliftonLarsonAllen
401(k)
United States, Georgia, Atlanta
3575 Piedmont Road (Show on map)
Dec 13, 2025

CLA is a top 10 national professional services firm where our purpose is to create opportunities every day, for our clients, our people, and our communities through industry-focused wealth advisory, digital, audit, tax, consulting, and outsourcing services. Even with more than 8,500 people, 130 U.S. locations, and a global reach, we promise to know you and help you.

CLA is dedicated to building a culture that invites different beliefs and perspectives to the table, so we can truly know and help our clients, communities, and each other.

CLA is looking to hire a Data Scientist Director to join our growing team.

About the role:

As a Data Scientist Director, you will: constructs complex solutions that integrate data wrangling, visualization, and advanced modeling techniques into a seamless workflow using software development best practices in Python, R, or other scripting languages. The role includes working within modern data platforms, such as Databricks, to develop and optimize scalable data pipelines and machine learning models, automating business workflows and answering business questions to improve CLA by integrating ML or AI algorithms into our decision-making processes, and working with and building AI/ML models, including utilizing and/or tuning Large Language Models (LLMS). This role develops more autonomy to develop solutions and may lead others, take on administrative tasks, perform support roles, and get involved in new program/project development with our client-facing teams.

How You'll Create Opportunities In This Data Scientist Director role

  • Service Specialization: Develop service specific knowledge through greater exposure to peers, internal experts, clients, regular self-study, and formal training opportunities. Gain exposure to a variety of client situations to develop business skills. Retain knowledge gained and performance feedback provided to transfer into future work. Approach all problems and projects with a high level of professionalism, objectivity and an open mind to new ideas and solutions.
  • Data Analysis: Participate and take ownership in the collection, analysis, and automated collection of data using a variety of data tools. Together with the Data Analytics team, support the building and implementing of models, algorithms, and simulations supporting solutions for external clients and internal projects. Assess the effectiveness and accuracy of new data sources and data gathering techniques.
  • Data Development: Working under the guidance of a variety of Data Analytics team members, gain exposure to developing custom data models and algorithms to apply to data sets. Execute predictive and inferential analytics, machine learning, and artificial intelligence techniques. Use existing processes and tools to monitor and analyze solution performance and accuracy, and communicate findings to team members and end users.
  • *Collaboration: Work individually as well as in collaboration with others. Interaction with others will primarily be virtual with leadership and colleagues from other offices. Take on additional roles beyond technical development and client service that may include: serving as primary contact with clients or business leaders on internal projects, take on administrative tasks, perform support roles, and get involved in new business development.
  • Collaboration: Work individually as well as in collaboration with others. Interaction with others will primarily be virtual with leadership and colleagues from other offices. Take on additional roles beyond technical development that may include: serving as primary contact with business leaders on internal projects, take on administrative tasks, perform support roles, and get involved in new program/project development.

What You Will Need

  • Two years of relevant experience required.
  • Experience in data analytics, statistics, data science, financial consulting, computer science or related field preferred.
  • Experience with APIs, web scraping, SQL/no-SQL databases, and cloud-based data solutions preferred.

Education

  • Bachelor's degree is required. Combination of relevant experience, education, and training may be accepted in lieu of degree.
  • Degree in field of Statistics, Data Science (e.g., Informatics, Data Science, Health Data Science), Computer Science, Economics, Analytics, or Data Science (e.g., Informatics, Data Science, Health Data Science) preferred.
  • Master's degree preferred.

Technical Competencies

Knowledge and expertise in data science and statistical computer languages (Python, R, SQL, etc.) to manipulate data and draw insights from large data sets. Experience working with and creating data architectures or schemas. Demonstrated knowledge of machine learning and AI models, including Large Language Models (LLMs) such as GPT, Llama and Claude. Experience in fine-tuning, deploying, and maintaining these models in production environment as well as other statistical modeling techniques and their real-world advantages/drawbacks.

* Understanding the domain specific nature of data being collected/analyzed and how data may be utilized to satisfy project objectives.

* Ability to integrate vision models and extraction models into workflows to enhance data collection, processing, and insights, in addition to leveraging LLMs and other AI models for predictive analytics and automation.

* Experience in developing and optimizing data pipelines for machine learning and AI model training and evaluation. Familiarity with cloud-based services for scalable AI/ML deployments.

* Experience with vision models and data extraction algorithms to automate processes such as document parsing, object detection, and structured data extraction from images or unstructured data.

* Experience in data manipulation using tools like Python, R, and/or SQL, with a focus on preparing data for machine learning applications. Ability to harmonize disparate data sources for use with AI/ML models.

* Experience with a variety of machine learning models and dimension reduction techniques including but not limited to: linear/logistic regression and other generalized linear models, tree based methods such as CARTs, random forests, boosting, SVMs, penalized methods such as ridge and LASSO (elastic nets), PCA, t-SNE, clustering methods, and other methods that can be applied to create predictive or inferential/descriptive models.

* Ability to code in Python, R, SQL, and experience with Databricks is a plus.

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Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities

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Wellness at CLA

To support our CLA family members, we focus on their physical, financial, social, and emotional well-being and offer comprehensive benefit options that include health, dental, vision, 401k and much more.

To view a complete list of benefits click here.

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