Senior Analyst

Responsibilities:

• Responsible for the design, development, programming methods, processes and systems to consolidate and analyse unstructured, diverse big data sources to generate actionable insights and solutions for businesses across the company.
• Work with business stakeholders and cross-functional SMEs to deeply understand business context and key business questions.
• Advanced skills with statistics/programming in Python and data querying languages (e. g., SQL, Hadoop/Hive, and Scala).
• Solid understanding of time-series forecasting techniques.
• Good hands-on skills in both feature engineering and hyperparameter optimisation.
• Able to write clean and tested code that can be maintained by other software engineers.
• Able to clearly summarise and communicate data analysis assumptions and results.
• Able to craft effective data pipelines to transform your analyses from offline to production systems.
• Self-motivated and a proactive problem solver who can work independently and in teams.
• Connects both externally and internally to understand industry trends, technology advances and outstanding processes or solutions.
• Is collaborative and engages strategically and tactically. Able to influence without authority, handle complex issues and implement positive change.
• Work on multiple pillars of AI, including cognitive engineering, conversational bots, and data science.
• 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 realise their full potential.
• Lead discussions at peer review and use interpersonal skills to positively influence decision-making.
• Provide 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.

Requirements:

• Master's degree in a quantitative field such as data science, statistics or applied mathematics or a bachelor's degree in engineering, computer science or a related field.
• 4 - 6 years of total work experience as a data scientist or in an analytical role, with at least 2-3 years of experience in time series forecasting.
• A combination of business focus, strong analytical and problem-solving skills, and programming knowledge to be able to quickly cycle hypotheses through the discovery phase of a project.
• Strong experience in time series forecasting and demand planning.
• Advanced skills with statistical/programming software (e. g., R and Python) and data querying languages (e. g., SQL, Hadoop/Hive, and Scala).
• Good hands-on skills in both feature engineering and hyperparameter optimisation.
• Experience producing high-quality code, tests, and documentation.
• Understanding of descriptive and exploratory statistics, predictive modelling, evaluation metrics, decision trees, machine learning algorithms, optimisation and forecasting techniques, and/or deep learning methodologies.
• Proficiency in statistical concepts and ML algorithms [GPT, Time Gen HOld Winter, Linear Regression, XGBoost].
• Ability to lead, manage, build, and deliver customer business results through data scientists or professional services teams.
• Ability to share ideas in a compelling manner and to clearly summarise and communicate data analysis assumptions and results.
• Self-motivated and a proactive problem solver who can work independently and in teams.
• Outstanding verbal and written communication skills with the ability to effectively advocate technical solutions to engineering and business teams.
• Experience working in one or multiple supply chain functions (e. g., procurement, planning, manufacturing, quality, and logistics) is strongly preferred.
• Experience in applying AI/ML within a CPG or healthcare business environment is strongly preferred.
• Experience in creating CI/CD pipelines for deployment using Jenkins.
• Experience implementing the MLOps framework along with an understanding of data security.
• Implementation of ML models.
• Exposure to visualisation packages and the Azure tech stack.