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.Key ResponsibilitiesBusiness Understanding & Problem SolvingCollaborate 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  Data Engineering & Pipeline ManagementCreate 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.Statistical Analysis & ReportingPerform 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.Model Development & ML OpsHas 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 & GitHub/GitLab:, model deployment & cloud MLOps tools such as Docker, SageMaker, Azure ML.