Citrin Cooperman offers a dynamic work environment, fostering professional growth and collaboration. We’re continuously seeking talented individuals who bring a problem-solving mindset, fresh perspectives, and sharp technical expertise. We know you have choices, so our team of collaborative, innovative professionals are ready to support your professional development. At Citrin Cooperman, we offer competitive compensation and benefits and most importantly, the flexibility to manage your personal and professional life to focus on what matters most to you!

We are seeking a Data Operations Scientist, Development, to join our Development team within the Information Technology department. While our parallel AI Solutions team focuses on Generative AI and Agentic pilots, we’re seeking a dedicated Data Operations Scientist to own our core predictive analytics, statistical modeling, and traditional Machine Learning (ML) capabilities.

In this role, you’ll be the analytical powerhouse of our “Base Plan.” You’ll work directly with the Database Administrator and Data Engineers to ensure our Medallion architecture (bronze, silver, gold layers) is optimized not just for BI reporting, but for feature engineering and model training at scale. Utilizing Microsoft Fabric’s Synapse and Databricks, you’ll design, train, and deploy robust ML models that solve tangible business problems, including but not limited to customer churn prediction, demand forecasting, and operational optimization. The ideal candidate is a pragmatic statistician and coder who values MLOps discipline, model interpretability, and stable production deployments over experimental hype.

Responsibilities are, but not limited to:

Predictive Modeling & Advanced Analytics: Design, train, and validate traditional machine learning models (e.g., regression, classification, clustering, time-series forecasting) using Python, PySpark, and established libraries (Scikit-Learn, XGBoost, LightGBM).
Feature Engineering & Data Shaping: Partner closely with Data Engineers to design the “Gold” data layer. Create and manage robust feature pipelines, ensuring data is properly structured, normalized, and optimized for both training and low-latency inference.
MLOps & Model Lifecycle Management: Deploy models into production within the Microsoft Fabric ecosystem. Establish the MLOps pipelines required to track model versions (e.g., using MLflow), monitor for concept/data drift, and trigger automated retraining when performance degrades.
Exploratory Data Analysis (EDA): Conduct deep-dive statistical analyses on large, complex enterprise datasets (housed in OneLake/SQL) to uncover hidden patterns, validate business hypotheses, and inform strategic decision-making.
Collaboration & Translation: Act as the bridge between raw data and business strategy. Translate complex statistical outcomes into clear, actionable insights for non-technical stakeholders, often partnering with BI developers to integrate model outputs into Power BI dashboards.
Algorithm Governance: Document model methodologies, assumptions, and limitations to ensure compliance with enterprise data governance and algorithmic fairness standards.

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See also