At least 6+ years of professional experience in implementing Analytics for business decision support. Experience in implementing statistical/machine learning models like regression (linear, non-linear, mixed models and Bayesian models), classification and unsupervised models i.e., K-Means clustering and Hierarchical clustering in solving key business problems and delivered measurable impact. Clear understanding of statistical concepts and understanding of the underlying assumptions of the models
Good hands onexperience in Python/R, SQL, visualization tools (PowerBI or Tableau), MS Excel, MS-power point (for delivering insights and results)
Collaborating with engineering teams to develop prototypes and software products that can be deployed across varied environments (e.g., cloud, on-premises, devices, etc.)
Implementation experience in at least two topics A. Deep learning concepts (NLP or CV), B. Cognitive cloud APIs from AWS/Azure, C. Docker and D. MLOPs is preferred
Experience in business analytics in pharmaceutical/CPG Company or KPO.
Ability to handle various data sources (format – structured and unstructured; volume – sparse and large), using data modeling to develop reporting capabilities and valuable insights across these data sources is key.
Provide increased focus on analytics and emphasize on providing valuable insights to drive improvement opportunities
Automate data preparation via data pipelines and insights/report creation where possible
Ability to comprehend business needs, convert them into BRD & TRD (Business/Technical requirement document), develop implementation roadmap and execute on time
Effectively respond to requests for ad hoc analyses.
Good verbal and written communication skills
Ownership of tasks assigned without supervisory follow-up
Proactive planner and can work independently to manage own responsibilities
Personal drive and positive work ethic to deliver results within tight deadlines and in demanding situations
Master’s or bachelor’s in engineering - BE/B- Tech, BCA, MCA, BSc/MSc