Data Science Manager

Blend360•Columbia, MD

About The Position

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. 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.

Requirements

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 APIs or 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

Nice To Haves

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

Responsibilities

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 performance, measure campaign effectiveness, and increase engagement and conversion rates.
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.