Assistant Manager - Data Science

Major Duties & Responsibilities
Work with business stakeholders and cross-functional SMEs to deeply understand business context and key business questions
Create Proof of concepts (POCs) / Minimum Viable Products (MVPs), then guide them through to production deployment and operationalization of projects
Influence machine learning strategy for Digital programs and projects
Make solution recommendations that appropriately balance speed to market and analytical soundness
Explore design options to assess efficiency and impact, develop approaches to improve robustness and rigor
Develop analytical / modelling solutions using a variety of commercial and open-source tools (e.g., Python, R, TensorFlow)
Formulate model-based solutions by combining machine learning algorithms with other techniques such as simulations.
Design, adapt, and visualize solutions based on evolving requirements and communicate them through presentations, scenarios, and stories.
Create algorithms to extract information from large, multiparametric data sets.
Deploy algorithms to production to identify actionable insights from large databases.
Compare results from various methodologies and recommend optimal techniques.
Design, adapt, and visualize solutions based on evolving requirements and communicate them through presentations, scenarios, and stories.
Develop and embed automated processes for predictive model validation, deployment, and implementation
Work on multiple pillars of AI including cognitive engineering, conversational bots, and data scienc
Ensure that solutions exhibit high levels of performance, security, scalability, maintainability, repeatability, appropriate reusability, and reliability upon deployment
Provide guidance and leadership to more junior data scientists, managing processes and flow of work, vetting designs, and mentoring team members to realize their full potential
Lead discussions at peer review and use interpersonal skills to positively influence decision making
Provide thought leadership and subject matter expertise in machine learning techniques, tools, and concepts; make impactful contributions to internal discussions on emerging practices
Facilitate cross-geography sharing of new ideas, learnings, and best-practices
Required Qualifications
Bachelor of Science or Bachelor of Engineering at a minimum.
7 years of work experience as a Data Scientist
A combination of business focus, strong analytical and problem-solving skills, and programming knowledge to be able to quickly cycle hypothesis through the discovery phase of a project
Advanced skills with statistical/programming software (e.g., R, Python) and data querying languages (e.g., SQL, Hadoop/Hive, Scala)
Good hands-on skills in both feature engineering and hyperparameter optimization
Experience producing high-quality code, tests, documentation Experience with Microsoft Azure or AWS data management tools such as Azure Data factory, data lake, Azure ML, Synapse, Databricks
Understanding of descriptive and exploratory statistics, predictive modelling, evaluation metrics, decision trees, machine learning algorithms, optimization & forecasting techniques, and / or deep learning methodologies
Proficiency in statistical concepts and ML algorithms Good knowledge of Agile principles and process
Ability to lead, manage, build, and deliver customer business results through data scientists or professionalservices team
Ability to share ideas in a compelling manner, to clearly summarize and communicate data analysisassumptions and results
Self-motivated and a proactive problem solver who can work independently and in teams
Preferred Qualifications Experience working in one or multiple supply chain functions (e.g., procurement, planning, manufacturing, quality, logistics) is strongly preferred
Experience in applying AI/ML within a CPG or Healthcare business environment is strongly preferred
Exposure to Pyomo, Mosel, truck load optimization, multi-echelon inventory optimization (MEIO)
Experience in NLP, Vision, and/or AR / VR Experience in creating CI/CD pipelines for deployment using Jenkins.
Experience implementing MLOPs framework along with understanding of data security implementation on ML models
Hands-on experience developing ML driven models
Must have Skills
Data Science - 5 Years
Python - 3 Years
SQL - 3 Years
Hadoop - 3 Years
Apache Hive - 3 Years
Apache Scala - 3 Years
Azure - 2 Years
AWS - 2 Years
Machine Learning - 3 Years