Responsibilities
Data Analysis: Collect, clean, and preprocess large datasets to extract meaningful information.
Conduct exploratory data analysis to identify trends, correlations, and patterns.
Prescriptive Modeling: Develop and implement prescriptive models using statistical and machine learning techniques to generate actionable recommendations. Collaborate with cross-functional teams to understand business requirements and translate them into analytic solutions.
Decision Support: Provide decision support by translating analytical findings into clear, actionable insights for key stakeholders. Work closely with business leaders to understand their goals and challenges, offering data-driven solutions.
Optimization: Identify opportunities for process optimization and efficiency improvement based on data-driven insights. Implement algorithms and models to optimize business processes and resource allocation.
Communication: Present findings and recommendations in a clear and understandable manner to both technical and non-technical stakeholders. Collaborate with team members to ensure effective communication and understanding of analytical results.
Collaboration: Work closely with data engineers, business analysts, and other stakeholders to ensure data integrity and relevance of analytical models.
Knowledge And Experience
Proven experience in data analysis and prescriptive modeling.
Experience in programming languages such as Python or R is preferred.
Strong understanding of statistical concepts and machine learning algorithms.
Excellent communication skills with the ability to convey complex findings to both technical and non-technical audiences.
Experience with data visualization tools and techniques (Tableau, Power BI, etc.)
BS in Mathematics, Economics, Computer Science, Information Management or Statistics.
Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope
Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities
Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards
Use big data technologies to help process data and build scaled data pipelines (batch to real time)
Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines
Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure
Automate ML models deployments
Qualifications
8+ years of overall experience that includes at least 4+ years of hands-on work experience Data Science / Machine learning
Minimum 4+ year of SQL experience
Minimum 2+ years of Python and Pyspark experience
Experience in DevOps and 2+ yrs in Machine Learning (ML) with hands-on experience with one or more cloud service providers AWS, GCP, (Azure preferred) is preferred
BE/B.Tech in Computer Science, Maths, technical fields.
Stakeholder engagement-BU, Vendors.
Data Science Hands on experience and strong knowledge of building machine learning models supervised and unsupervised models. Knowledge of Time series/Demand Forecast models is a plus
Programming Skills Hands-on experience in statistical programming languages like Python, Pyspark and database query languages like SQL
Statistics Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators
Cloud (Azure) Experience in Databricks and ADF is desirable
Familiarity with Spark, Hive, Pig is an added advantage
Model deployment experience will be a plus
Experience with version control systems like GitHub and CI/CD tools
Experience is Exploratory data Analysis
Knowledge of ML Ops / DevOps and deploying ML models is required
Experience using MLFlow, Kubeflow etc. will be preferred
Experience executing and contributing to ML OPS automation infrastructure is good to have
Exceptional analytical and problem-solving skills
Experience building statistical models in the Commercial, Net revenue Management or Supply chain space is a plus
Differentiating Competencies Required:
Ability to work with virtual teams (remote work locations); collaborate with technical resources (employees and contractors) based in multiple locations across geographies
Participate in technical discussions, driving clarity of complex issues/requirements to build robust solutions
Strong communication skills to meet with business, understand sometimes ambiguous, needs, and translate to clear, aligned requirements
Able to work independently with business partners to understand requirements quickly, perform analysis and lead the design review sessions
Highly influential and having the ability to educate challenging stakeholders on the role of data and its purpose in the business
Places the user in the centre of decision making
Teams up and collaborates for speed, agility, and innovation
Experience with and embraces agile methodologies
Strong negotiation and decision-making skill
Experience managing and working with globally distributed teams
Keywords: continuous integration continuous deployment machine learning business intelligence rlang
Data BI Analyst