Marketing Data Scientist / Analyst (direct hire, remote)

Overview Seeking a U.S.-based Marketing Data Scientist / Analyst (*based on client requirements, candidates must be U.S. citizen or Green Card holder) to apply analytical and technical skills to real-world business problems. Partner with stakeholders to collect, clean, analyze, and interpret data; build, validate, deploy, and monitor ML models; and communicate insights that drive product features and operational decisions. Role spans supervised and unsupervised learning, ensemble and time-series methods, experimentation, and documentation across multiple concurrent projects. Type: Direct hire, remote Schedule: Regular day hours Pay: $128,000 - $137,000 salary Marketing Data Scientist / Analyst Requirements • Bachelor’s or Master’s in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or related field • 3+ years in data analysis, statistical modeling, or machine learning • Strong grounding in statistics, probability, experimental design; ability to work with imperfect data • Proficiency in Python or R; version control (Git) preferred • Data wrangling with pandas, NumPy, SciPy (Python) or dplyr, tidyr (R) • Visualization with Matplotlib, Seaborn, Plotly (Python) or ggplot2 (R) • SQL and database fundamentals • Familiarity with ML algorithms: regression, classification, clustering, dimensionality reduction; ensembles and time-series a plus • Cloud experience (AWS/Azure/GCP) preferred; big data (Spark, Hadoop) preferred • Experience deploying ML to production preferred • Excellent written and verbal communication; ability to explain technical concepts to non-technical audiences • Demonstrated independence, collaboration, and continuous learning mindset • Based on client requirements, candidates must be U.S. citizen or Green Card holder Marketing Data Scientist / Analyst Duties • Collect, clean, and preprocess large, multi-source datasets; define data quality checks and remediation steps • Perform EDA to surface patterns, trends, anomalies, and opportunities; frame hypotheses and test plans • Build, validate, and tune supervised/unsupervised, ensemble, and time-series models; conduct feature engineering and model diagnostics • Design and analyze experiments; create and maintain KPIs and model performance dashboards • Document code, methods, assumptions, and results; produce clear visuals, reports, and presentations for stakeholders • Collaborate with data scientists, engineering, product, and business teams to refine objectives and success metrics • Package, deploy, and monitor models; set up alerting, retraining, and drift checks; contribute to MLOps best practices • Evaluate new data sources, tools, and techniques; prototype and productionize data-driven product features • Plan and execute work across multiple projects and functions; manage timelines, risks, and deliverables; stay current with advances in ML and data science No deadline to apply.