Data Science Manager

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.

• 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.
• Skills to 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.

• Has experience on building predictive models in business applications, Understand modern machine learning algorithms and best practices.
• Familiarie with model algorithm version control tools such as Git & Git Hub/Git Lab:, model deployment & cloud MLOps tools such as Docker, Sage Maker, Azure ML.

• 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.

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