Data Science Manager

Blend360 • Columbia, MD

163d

About The Position

We are seeking a versatile Data Scientist with experience in ML Ops and data engineering. This role will drive advanced analytics solutions working closely with both internal practice leaders and client stakeholders.

Requirements

• 5+ years of hands-on experience in Data Science, including model building and ML Ops.
• 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

• Collaborate with practice leaders and client teams to understand business problems, industry context, data sources, constraints, and risks.
• Translate complex business challenges into actionable Data Science solutions, proposing multiple analytical approaches with pros and cons.
• Gather stakeholder feedback, gain alignment on methods, deliverables, and roadmaps.
• Lead and manage large size projects that involve cross discipline team members and 3+ months project duration.
• Create and maintain robust data pipelines, integrating internal and external data sources using tools like SQL, Spark, and cloud big data platforms (AWS, Azure, or GCP).
• Assemble and transform large, complex datasets to meet functional business and modeling requirements.
• Conduct data cleaning, quality control (QC), and diagnostic analysis to assess data integrity.
• Perform exploratory data analysis (EDA), A/B Test, data mining, and statistical modeling to extract actionable insights.
• Summarize data characteristics and identify potential data issues for stakeholders and decision-makers.
• Contribute to written and visual documentation of insights, models, and analytical findings.
• Build predictive models in business applications, understand modern machine learning algorithms and best practices.
• Familiarity with model algorithm version control tools such as Git & GitHub/GitLab, model deployment & cloud MLOps tools such as Docker, SageMaker, Azure ML.

What This Job Offers

• Number of Employees: 1,001-5,000 employees