Assistant Manager, Global Data Science

An expert coder to write codes in SQL, Python to build scalable enterprise level data science solutions in Marketing Mix Modelling and Revenue Growth Management and to address business problems requiring descriptive, diagnostic, predictive, and / or prescriptive analytics
Ability to write codes to build Regression Models, Machine Learning Models [ Ridge, Lasso, Logistic Regression, Bayesian Regression, Random Forests, XGBOOST, Optimization, Simulation ] at scale and then deploy models in production on cloud platforms using Airflow
Leading, Mentoring, Coaching data scientists on key technical and domain topics through solution and code reviews to meet business objectives
End to end accountability of quality of recommendations, insights and data science solutions developed and deployed in the organization globally
You can prioritize multiple work assignments multi-task conflicting priorities and meet deadlines
Excellent written and verbal communication.
Good facilitation and project management skills

You connect the dots
- Understands business needs, business questions and identifies the right analytics capabilities to solve them
Presentation and Storytelling of model results, insights, recommendation and actions to Marketing and Customer Development (Sales) team and to Division/Global Analytics Leads
Communicate complex quantitative insights in a precise, and actionable manner to business stakeholders

You are a collaborator
- Work with cross functional teams in GIT, Data Architecture, Analytics Engineering to drive the delivery and project management of large scale enterprise wide data science solutions
Work collaboratively with the Division Analytics/Global Analytics team and business partners across geographies located in different time zones.

You are an innovator
- Drive new value from insights by connecting external and internal data sources
Bring in enhancements to the existing data science solutions / capabilities by exploring new solutions and techniques
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, exploring new data sources, re-designing infrastructure for greater scalability, etc.

Qualifications
What you'll need
Bachelor degree required [ Masters or MBA preferred ] in data science, computer science engineering, Information Technology, other engineering streams, a related quantitative and technical (Statistics, Business Analytics, Econometrics)
8+ years of data science and analytics experience in developing and deploying machine learning algorithms in a production environment
Experience in setting up or managing a data science team is a must [ 2+ years ]
Expert knowledge in all the following domains Marketing Mix Modeling, Revenue Growth Management, Optimization Techniques, Forecasting, Recommendation Engines, Marketing Analytics in CPG or Consulting Companies
Extraordinary coding skills in SQL, Python, PySpark, R to implement statistical and machine learning algorithms like Linear Regression, Ridge, Lasso, PyMc, Bayesian Methods, GLMNET, Decision Trees, Random Forests, XGBOOST, SVM etc
Strong knowledge of MLOPS using docker, airflow, kubernetes, databricks, dataiku on any cloud platform
Proficient in Cloud Platforms - Google Cloud, Snowflake and experience dealing with high-volume, high-dimensionality data from varying sources (Cloud Platforms, Nielsen, SAP, Retailer ePOS; both structured and unstructured data)
Experience in supporting and working with multi-functional teams in a dynamic environment
Proficient in Reporting Insights, making and delivering presentation via Excel, Google Sheets, Google Slides, DOMO and any other Visualization Tools
Proven Ability to provide structure and think strategically in complex business context and operate effectively in ambiguity
Ability to independently plan and execute deliveries and be comfortable in a client / business facing role
Exceptional communication & collaboration skills to understand business partner needs & deliver solutions.

What you'll need (Preferred)
Experience in building web apps using Pydash, Plotly, R Shiny, Streamlit
Experience in Deep Learning, Reinforcement Learning, Image Recognition, AI algorithms, GenAI

Experience with Databricks, Docker, Azure Data Factory

Experience with machine learning API on cloud services:
Azure, AWS, Vertex AI