Data Operations Scientist, Development (52350)

Job DetailsPosition Type: Full Time / Experienced LevelSalary Range: $145,000.00 - $175,000.00 Salary/yearJob Category: Corporate ITCitrin 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. QualificationsThe ideal candidate must: Have a bachelor’s degree in computer science, data engineering, mathematics, or equivalent practical experience. Have 3–5 years of professional experience as a Data Scientist, Machine Learning Engineer, or Advanced Analyst in a corporate environment. Have deep proficiency in Python and SQL, with strong hands-on experience using industry-standard data science and ML libraries (Pandas, NumPy, Scikit-Learn, PyTorch/TensorFlow). Have proven experience with Big Data processing frameworks (Apache Spark, PySpark) and modern cloud data platforms (Microsoft Fabric, Databricks, or Azure Machine Learning heavily preferred). Possess a solid foundation in statistics, probability, and mathematics, with the ability to mathematically justify model selection and evaluation metrics (RMSE, F1-score, AUC-ROC). Have experience implementing MLOps best practices, including model registry management, containerized deployments, and performance monitoring. Possess strong business acumen and the ability to connect statistical improvements directly to business ROI. Be pragmatic problem solver: Chooses the simplest, most explainable model (like a well-tuned random forest) that solves the business problem, rather than over-engineering a complex neural network just for the sake of it. Be rigorous & methodical: Deeply respects data quality and understands that a model is only as good as the pipelines feeding it. Naturally skeptical of “perfect” training results. Be a cross-functional collaborator: Thrives in a team setting. Eager to sit down with a Data Engineer to optimize a Spark query or with a TPM to scope a sprint, rather than working in an isolated research silo. Be Microsoft certified: Azure Data Science Associate (DP-100) (preferred). Be Microsoft certified: Fabric Analytics Engineer Associate (DP-600) (preferred). Be Databricks certified: Machine Learning Associate (PL-300) (preferred).