Senior Level Product Data Specialist Jobs
Senior level product data specialist jobs place experienced professionals at the center of product analytics strategy, owning data infrastructure decisions, leading cross-functional initiatives, and guiding the teams that turn raw data into measurable product outcomes. Openings concentrate in Technology & Software, Consulting & Professional Services, and Banking & Financial Services, with 41% of roles offered remotely or hybrid, and employers like Capital One, Oracle, and JPMorganChase hiring at this level now.
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Showing 5 of 879+ Senior Level Product Data Specialist jobs
Job Title: Data Scientist 8+ years
Location: New Jersey
Employment Type: Full-Time
About the Role:
We are seeking a highly skilled and experienced Data Scientist to develop and implement advanced machine learning, statistical modeling, predictive analytics, and AI solutions that drive data-driven business decisions.
Key Responsibilities:
- Develop, train, validate, and deploy machine learning models for classification, regression, clustering, forecasting, recommendation, anomaly detection, and predictive analytics use cases.
- Perform exploratory data analysis (EDA), statistical analysis, data profiling, data preprocessing, data cleansing, and feature engineering to identify meaningful patterns and business insights.
- Build predictive models using Python, Scikit-learn, Pandas, NumPy, XGBoost, LightGBM, and other machine learning frameworks.
- Apply advanced statistical modeling, hypothesis testing, regression analysis, probability, time-series analysis, correlation analysis, and experimental design to solve complex business problems.
- Develop and optimize Deep Learning models using frameworks such as TensorFlow, PyTorch, Keras, and related AI/ML technologies.
- Design and implement Natural Language Processing (NLP) solutions for text classification, sentiment analysis, document processing, information extraction, text summarization, and semantic analysis.
- Work with Generative AI, Large Language Models (LLMs), embeddings, vector databases, prompt engineering, Retrieval-Augmented Generation (RAG), and AI-powered applications where applicable.
- Develop end-to-end data science solutions, from data collection and preprocessing through model development, evaluation, deployment, monitoring, and continuous improvement.
- Evaluate model performance using appropriate evaluation metrics, cross-validation, hyperparameter tuning, model selection, explainability, and statistical validation techniques.
- Implement MLOps practices model versioning, experiment tracking, deployment automation, model monitoring, drift detection, and lifecycle management.
- Work with cloud-based AI/ML platforms such as Microsoft Azure, Azure Machine Learning, Azure Databricks, Azure Data Lake, and Azure Synapse Analytics.
- Develop scalable data processing and machine learning workflows using Apache Spark, PySpark, Databricks, and distributed computing technologies.
- Create dashboards, visualizations, and analytical reports using Power BI, Tableau, Matplotlib, Seaborn, or other visualization tools to communicate insights to business stakeholders.
Required Skills & Qualifications:
- 8+ years of experience in Data Science, Machine Learning, Predictive Analytics, Statistical Modeling, and Advanced Analytics.
- Strong programming expertise in Python, with hands-on experience using Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, and Seaborn.
- Extensive knowledge of Machine Learning algorithms, including Linear/Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, XGBoost, LightGBM, SVM, K-Means, Clustering, PCA, and Ensemble Methods.
- Hands-on experience with Deep Learning frameworks such as TensorFlow, PyTorch, and Keras, including neural networks and model optimization.
- Experience with Natural Language Processing (NLP), including text classification, sentiment analysis, tokenization, embeddings, transformers, and language models.
- Strong understanding of Generative AI, Large Language Models (LLMs), Prompt Engineering, RAG, Vector Databases, Embeddings, and AI application development.
Job Title: Data Scientist 8+ years
Location: New Jersey
Employment Type: Full-Time
About the Role:
We are seeking a highly skilled and experienced Data Scientist to develop and implement advanced machine learning, statistical modeling, predictive analytics, and AI solutions that drive data-driven business decisions.
Key Responsibilities:
- Develop, train, validate, and deploy machine learning models for classification, regression, clustering, forecasting, recommendation, anomaly detection, and predictive analytics use cases.
- Perform exploratory data analysis (EDA), statistical analysis, data profiling, data preprocessing, data cleansing, and feature engineering to identify meaningful patterns and business insights.
- Build predictive models using Python, Scikit-learn, Pandas, NumPy, XGBoost, LightGBM, and other machine learning frameworks.
- Apply advanced statistical modeling, hypothesis testing, regression analysis, probability, time-series analysis, correlation analysis, and experimental design to solve complex business problems.
- Develop and optimize Deep Learning models using frameworks such as TensorFlow, PyTorch, Keras, and related AI/ML technologies.
- Design and implement Natural Language Processing (NLP) solutions for text classification, sentiment analysis, document processing, information extraction, text summarization, and semantic analysis.
- Work with Generative AI, Large Language Models (LLMs), embeddings, vector databases, prompt engineering, Retrieval-Augmented Generation (RAG), and AI-powered applications where applicable.
- Develop end-to-end data science solutions, from data collection and preprocessing through model development, evaluation, deployment, monitoring, and continuous improvement.
- Evaluate model performance using appropriate evaluation metrics, cross-validation, hyperparameter tuning, model selection, explainability, and statistical validation techniques.
- Implement MLOps practices model versioning, experiment tracking, deployment automation, model monitoring, drift detection, and lifecycle management.
- Work with cloud-based AI/ML platforms such as Microsoft Azure, Azure Machine Learning, Azure Databricks, Azure Data Lake, and Azure Synapse Analytics.
- Develop scalable data processing and machine learning workflows using Apache Spark, PySpark, Databricks, and distributed computing technologies.
- Create dashboards, visualizations, and analytical reports using Power BI, Tableau, Matplotlib, Seaborn, or other visualization tools to communicate insights to business stakeholders.
Required Skills & Qualifications:
- 8+ years of experience in Data Science, Machine Learning, Predictive Analytics, Statistical Modeling, and Advanced Analytics.
- Strong programming expertise in Python, with hands-on experience using Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, and Seaborn.
- Extensive knowledge of Machine Learning algorithms, including Linear/Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, XGBoost, LightGBM, SVM, K-Means, Clustering, PCA, and Ensemble Methods.
- Hands-on experience with Deep Learning frameworks such as TensorFlow, PyTorch, and Keras, including neural networks and model optimization.
- Experience with Natural Language Processing (NLP), including text classification, sentiment analysis, tokenization, embeddings, transformers, and language models.
- Strong understanding of Generative AI, Large Language Models (LLMs), Prompt Engineering, RAG, Vector Databases, Embeddings, and AI application development.
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Find JobsSenior Level Product Data Specialist Job Market
Who's Hiring
- Capital One27
- Oracle15
- JPMorganChase14
- Meta13
- Amazon13
Top Industries Hiring
- Technology & Software324
- Consulting & Professional Services88
- Banking & Financial Services58
- Construction & Real Estate37
- Investment & Asset Management35
Senior Level Product Data Specialist Jobs: Frequently Asked Questions
How do I get a senior level product data specialist job?
Employers at this level expect candidates who can own a data domain end-to-end, not just execute tasks. Highlight experience defining data models, influencing product roadmaps with analysis, and mentoring junior analysts. A strong portfolio of cross-functional projects, demonstrated ability to communicate data findings to non-technical stakeholders, and depth in tools like SQL, Python, and product analytics platforms will give you a clear edge.
Which companies hire senior level product data specialists?
Companies hiring senior level product data specialists right now include Capital One, Oracle, and JPMorganChase, based on current listings on Migrate Mate as of November 2026. Hiring at this level tends to come from organizations with mature data functions, including large technology firms, fintech companies, and enterprise software businesses that rely on product analytics to drive growth decisions.
Are there remote senior level product data specialist jobs?
Yes, remote and hybrid availability is strong at this level given the nature of the work. About 41% of senior level product data specialist openings are remote or hybrid as of November 2026, reflecting how many organizations have built distributed data teams. Senior roles often come with more flexibility because employers prioritize expertise over location when filling specialized positions.
What makes a product data specialist role senior level?
Senior level product data specialist roles are defined by scope, ownership, and influence rather than task execution. These positions typically involve setting the direction for how product data is collected, structured, and interpreted across a business unit, acting as a subject matter authority, and mentoring mid-level analysts. Decisions made at this level directly shape product strategy, making technical depth and cross-functional leadership equally important.
Which industries hire the most senior level product data specialists?
Senior level product data specialist roles concentrate in Technology & Software, Consulting & Professional Services, and Banking & Financial Services, based on current listings on Migrate Mate as of November 2026. These sectors drive hiring at this level because they operate data-intensive product ecosystems where senior specialists are needed to maintain data quality, build scalable pipelines, and connect product behavior insights to business outcomes.