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.
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.
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 SageMaker.
Experience working in Agile environments with cross-functional teams.