Marketing Data Science Manager

National Science Teachers Association; Columbia (MD); On-site; USD 125,000 - 160,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

401K

PTO

Job summary

BLEND360 is seeking a Data Science Manager with AI familiarity to lead end-to-end data science initiatives and production-grade GenAI projects. You will collaborate with practice leaders, engineers, and stakeholders to solve complex business challenges using data science and AI-driven approaches.

You will design and deploy AI solutions, optimize pipelines, and ensure robust, scalable capabilities across client environments, with a strong emphasis on governance, performance, and cost efficiency.

Qualifications

• 5+ years of hands-on data science experience including model building and ML Ops.
• Experience in email marketing and direct marketing.
• Experience managing people.
• Proficiency in Python, SQL, and tools like Pandas, Scikit-learn, NLTK/spaCy, Spark.
• Familiarity with digital marketing ecosystem and recommendation systems.
• Experience deploying models via APIs or integrating them into batch processing pipelines.
• Working knowledge of cloud data platforms (AWS S3, Redshift, GCP, Azure).
• Ability to manage data pipelines and ETL processes with data engineering practices.
• Strong communication and collaboration skills with clients.

Responsibilities

• Partner with leaders and clients to translate business needs into data science solutions.
• Evaluate approaches and communicate trade-offs clearly.
• Collaborate to align on methodology, deliverables, and roadmaps.
• Leverage ML and data analysis to optimize marketing campaigns.
• Run A/B tests to improve campaigns and conversions.
• Design and productionize AI solutions using LLMs, transformers, and RAG.
• Optimize prompts, workflows, and pipelines for performance and cost.
• Build multi-step agentic systems using external APIs/tools.
• Deploy GenAI models in production for scalability.
• Develop evaluation frameworks for grounding, latency, and cost.
• Implement safety measures such as prompt protection and moderation.
• Collaborate with Product, Engineering, and MLOps to deliver end-to-end capabilities.
• Create detailed project plans with milestones and risks.
• Build data pipelines using SQL, Spark, and cloud data tech.
• Integrate large datasets to support requirements.
• Create analytics tools for customer acquisition, operations, and metrics.
• Conduct explorations and modeling to inform decisions.
• Train and tune predictive models with modern ML techniques.
• Document model results for client delivery.

Skills

Python

SQL

Pandas

Scikit-learn

NLTK / spaCy

Spark

APIs

Cloud data platforms

Tools

Pandas

Scikit-learn

NLTK

spaCy

Spark

Job description

Company Description

Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com

Job Description

We are seeking a skilled and versatile Data Science Manager with AI familiarity to join our growing team. In this role, you’ll collaborate with practice leaders, engineers, and cross-functional stakeholders to solve complex business challenges using data science and AI-driven approaches. You’ll work on end-to-end data science initiatives, with opportunities to design and implement cutting-edge generative AI (GenAI) and LLM-powered solutions.

Key Responsibilities

Data Science & Analytics

• Partner with practice leaders and clients to understand business problems, industry context, data sources, risks, and constraints.
• Translate business needs into actionable data science solutions, evaluating multiple approaches and clearly communicating trade-offs.
• Collaborate with stakeholders to align on methodology, deliverables, and project roadmaps.
• Leverage Machine Learning and Data Analysis to optimize marketing campaigns
• Conduct A/B tests to improve campaign performancemeasure campaign effectiveness, and increase engagement and conversion rates.

AI & Generative AI Collaboration

In addition to traditional data science responsibilities, you will collaborate with AI and engineering teams to:

• Design and implement production-grade AI solutions leveraging LLMs, transformers, retrieval-augmented generation (RAG), agentic workflows, and generative AI agents.
• Optimize prompt design, workflows, and pipelines for performance, accuracy, and cost-efficiency.
• Build multi-step, stateful agentic systems that utilize external APIs/tools and support robust reasoning.
• Deploy GenAI models and pipelines in production (API, batch, or streaming) with a focus on scalability and reliability.
• Develop evaluation frameworks to monitor grounding, factuality, latency, and cost.
• Implement safety and reliability measures such as prompt-injection protection, content moderation, loop prevention, and tool-call limits.
• Work closely with Product, Engineering, and ML Ops to deliver robust, high-quality AI capabilities end-to-end.
• Develop and manage detailed project plans including milestones, risks, owners, and contingency plans.
• Create and maintain efficient data pipelines using SQL, Spark, and cloud-based big data technologies within client architectures.
• Collect, clean, and integrate large datasets from internal and external sources to support functional business requirements.
• Build analytics tools that deliver insights across domains such as customer acquisition, operations, and performance metrics.
• Perform exploratory data analysis, data mining, and statistical modeling to uncover insights and inform strategic decisions.
• Train, validate, and tune predictive models using modern machine learning techniques and tools.
• Document model results in a clear, client-ready format and support model deployment within client environments.

Qualifications

Required Skills & Experience

• 5+ years of hands‑on experience in Data Science, including model building and ML Ops
• Experience in email marketing and direct marketing
• Experience managing people
• Proficiency in Python, SQL, and tools like Pandas, Scikit-learn, NLTK/spaCy, and Spark
• Familiarity with digital marketing ecosystem (e.g., clickstream analytics) and recommendation systems
• Experience deploying models via APIsor integrating them into batch processing pipelines
• Working knowledge of cloud data platforms(e.g., AWS S3, Redshift, GCP, Azure)
• Ability to manage data pipelines and ETL processes with a solid understanding of data engineering best practices
• Strong communication and collaboration skills, including experience engaging directly with clients

Preferred Qualifications

• Exposure to ML Ops tools such as MLflow, Kubeflow, or SageMaker
• Experience working in Agile environments with cross-functional teams

Additional Information

The startingpayrange for this role is $125,000 - $160,000. Actual compensation within the range will be dependent on several factors including but not limited to relevant experience, skills, certifications, training, and location. It is not typical for an individual to be hired at or near the top of the range and determining factors for compensation are considered for each individual circumstance. BLEND360 also offers a competitive benefits program to meet the health and financial well‑being of our team and their families. You can look forward to a range of benefits including medical, dental, vision, 401K, PTO, paid holidays, commuter benefits, spending accounts, life insurance, disability coverage, and EAPs.