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Manager, Data Scientist; Sales and Marketing Platforms

Job in Shreveport, Caddo Parish, Louisiana, 71166, USA
Listing for: KPMG
Full Time position
Listed on 2026-10-01
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Analyst, Data Scientist
Job Description & How to Apply Below
Position: Manager, Data Scientist (Sales and Marketing Platforms)
Known for being a great place to work and build a career, KPMG provides audit, tax and advisory services for organizations in today's most important industries. Our growth is driven by delivering real results for our clients. It's also enabled by our culture, which encourages individual development, embraces an inclusive environment, rewards innovative excellence and supports our communities. With qualities like those, it's no wonder we're consistently ranked among the best companies to work for by Fortune Magazine, Consulting Magazine, Seramount, Fair
360 and others. If you're as passionate about your future as we are, join our team.

KPMG is currently seeking a Manager, Data Scientist (Sales and Marketing Platforms) to join our Clients and Markets organization.

Responsibilities:

Hands-on design, coding, and deployment of custom statistical algorithms, marketing lift models, and quasi-experimental frameworks in Python/R to quantify campaign impact and optimize commercial investments

Build and deploy machine learning models across Databricks and Salesforce, writing custom pipelines in Databricks MLflow/PySpark while configuring and tuning Salesforce Einstein / CRM Analytics ML models and Data Cloud transformationsScript and engineer AI-driven activation pipelines leveraging modern LLMs (Gemini, Claude), creating automated prompts and APIs to translate complex model outputs into narrative insights and conversational intelligence

Extract, clean, and engineer complex feature sets directly from raw CRM, digital touchpoint, and Lakehouse data sources, establishing seamless data/model integration between Databricks and Salesforce Perform rigorous model validation, back testing, hyperparameter tuning, and code maintenance, ensuring reproducible, high-performance execution across all production workloads; ensure all deployed scripts, models, and GenAI workflows comply with enterprise data governance, security, and responsible AI protocols

Act with integrity, professionalism, and personal responsibility to uphold KPMG's respectful and courteous work environment

Qualifications:

Minimum five years of recent deep, hands-on coding and modeling experience developing, evaluating, and deploying machine learning models in production environments

Master's degree from an accredited college or university preferred; minimum of a Bachelor's degree from an accredited college or university in statistics, data science, computer science, applied mathematics, or a related quantitative field is required

Expert-level Python and advanced SQL programming skills, with extensive hands-on experience using data science libraries (for example, pandas, Num Py, scikit-learn, PyMC, Stats models, LightGBM/XGBoost)
Proven hands-on track record building, training, and tracking models on Databricks (PySpark, Delta Lake, MLflow, Feature Store)
Hands-on experience developing, integrating, or tuning models within the Salesforce ecosystem (for example, Salesforce Einstein, CRM Analytics / Einstein Discovery, or Salesforce Data Cloud)
Applicants must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future; KPMG LLP will not sponsor applicants for U.S. work visa status for this opportunity (no sponsorship is available for H-1B, L-1, TN, O-1, E-3, H-1B1, F-1, J-1, OPT, CPT or any other employment-based visa)
KPMG LLP and its subsidiaries (“KPMG”) complies with all local/state regulations regarding displaying salary ranges. If required, the ranges displayed below or via the URL below are specifically for those potential hires who will work in the location(s) listed. Any offered salary is determined based on relevant factors such as applicant's skills, job responsibilities, prior relevant…
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