Senior Data Scientist - E-Commerce

Location: New York City, NY (On-site)

Note: This position is not eligible for Immigration Sponsorship at this time. We are seeking a highly motivated and analytical Senior Data Scientist with 4–6 years of experience in Machine Learning, Statistical Modeling, Predictive Analytics, and Data Science solutions. The ideal candidate will leverage advanced analytical techniques to extract insights from large and complex datasets, develop predictive models, and help drive data-driven decision-making across business functions. The role requires strong expertise in Python, SQL, machine learning algorithms, time-series forecasting, anomaly detection, and data storytelling. The candidate will work closely with business stakeholders, data engineers, product owners, and technology teams to solve critical operational and strategic challenges using data science methodologies. Experience with cloud and modern data platforms such as Databricks, Snowflake, Microsoft Fabric, and exposure to the C3 AI Platform will be highly preferred.

Data Science & Analytics: Develop, implement, and maintain machine learning models for classification, regression, clustering, recommendation systems, and predictive analytics. Design and execute advanced statistical analyses to identify business trends, risks, and opportunities. Perform exploratory data analysis (EDA) to uncover patterns and generate actionable insights from structured and unstructured datasets. Build forecasting models using time-series techniques for business planning, demand forecasting, asset performance prediction, and operational optimization. Develop anomaly detection models to identify unusual patterns, operational failures, fraud indicators, or equipment performance issues. Conduct root-cause analysis and hypothesis testing to support business decision-making.

Machine Learning Model Development: Design and implement end-to-end machine learning pipelines from data ingestion to deployment. Perform feature engineering, feature selection, and data transformation to improve model performance. Evaluate models using appropriate validation techniques, performance metrics, and cross-validation methodologies. Optimize machine learning algorithms for scalability, accuracy, and interpretability. Monitor model performance and implement retraining strategies to ensure sustained effectiveness.

Data Management & Engineering Collaboration: Partner with data engineering teams to build scalable data pipelines and analytical data models. Work with large datasets using SQL, distributed computing frameworks, and cloud-based analytics platforms. Ensure data quality, consistency, and governance standards are maintained throughout the analytics lifecycle. Translate complex data science concepts into business-friendly insights and actionable recommendations. Support strategic initiatives by providing data-driven forecasting and scenario analysis. Develop interactive dashboards and visualizations using Power BI, Tableau, or equivalent tools. Communicate model outcomes and business insights through compelling visual storytelling and executive presentations. MS or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, Operations Research, or a related quantitative field.~5+ years of applied machine learning, data science, or AI experience in a professional setting.~ Demonstrated experience owning projects or major workstreams from initial problem definition through delivery.~ Excellent programming skills in Python to include strong overall OOP experience.~ Strong proficiency with machine learning and data science tools such as Scikit-learn, PyTorch, TensorFlow, pandas, NumPy, XGBoost, or similar frameworks.~ Broad knowledge of machine learning and AI methods, including when to apply different approaches and what tradeoffs to consider.~ Project experience in several of the following areas: supervised learning, unsupervised learning, time series forecasting, deep learning, operations research, optimization, anomaly detection, or Generative AI.~ Familiarity with the machine learning lifecycle, including experimentation, versioning, deployment, monitoring, governance, and retraining.~ Experience with production-oriented ML tools or workflow platforms such as MLflow, Airflow, Kubeflow, Databricks, or similar.~ Familiarity with scalable data and AI technologies such as Spark, distributed processing, streaming systems, or cloud-based ML services.~ Prior exposure to AWS, Azure, or Google Cloud, especially AI/ML services, is advantageous.~ Hands-on experience with the C3 AI platform .Experience working in an embedded client environment, partner delivery model, enterprise SaaS implementation, or AI platform delivery role. Experience delivering AI solutions for large enterprises or Fortune 500 clients. Domain experience in manufacturing, energy, utilities, supply chain, aerospace, telecommunications, financial services, or other operationally complex industries. Experience with demand forecasting, asset reliability planning, predictive maintenance, inventory optimization, or supply chain optimization. The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions, including but not limited to skill sets; experience and training; In addition, you may be eligible for a discretionary bonus for the current performance period. As a full-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plans in accordance with the plan documents, which may be amended from time to time. The Company provides for 11 paid holidays and 12 weeks of Parental Leave. We also follow a "free time" PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation. Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.