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3 months ago
Data Science Team works in developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Specific scope of this role is to develop ML solution in support of ML/AI projects using big analytics toolsets in a CI/CD environment. Analytics toolsets may include DS tools/Spark/Databricks, and other technologies offered by Microsoft Azure or open-source toolsets. This role will also help automate the end-to-end cycle with Azure Machine Learning Services and Pipelines.
You will be part of a collaborative interdisciplinary team around data, where you will be responsible of our continuous delivery of statistical/ML models. You will work closely with process owners, product owners and final business users. This will provide you the correct visibility and understanding of criticality of your developments.
Responsibilities Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope Active contributor to code / development architect in projects and services Act as contributor in innovation activities Partner with data engineers to ensure data access for discovery and proper data is prepared for model consumption. Partner with ML engineers working on industrialization. Occasionally, coordinate work activities with Business teams, other IT services and as required. Communicate with business stakeholders in the process of service design, training and knowledge transfer. Support large-scale experimentation and build data-driven models. Set KPIs and metrics to evaluate analytics solution given a particular use case. Refine requirements into modelling problems. Influence product teams through data-based recommendations. Research in state-of-the-art methodologies. Create documentation for learnings and knowledge transfer. Create reusable packages or libraries. Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards Leverage big data technologies to help process data and build scaled data pipelines (batch to real time) Architect / Design end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines Automate ML models deployments Qualifications 8+ years working in a team to deliver production level analytic solutions. Fluent in git (version control). Understanding of Jenkins, Docker are a plus. 8+ years' experience in ETL and/or data wrangling techniques. Fluent in SQL syntaxis. 11+ years' experience in Statistical/ML techniques to solve supervised (regression, classification) and unsupervised problems. Experiences with Deep Learning are a plus. 8+ years' experience in developing business problem related statistical/ML modeling with industry tools with primary focus on Python or Scala development. BE/ in Computer Science, Maths, technical fields.Skills, Abilities, Knowledge:
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 Business storytelling and communicating data insights in business consumable format. Fluent in one Visualization tool. Strong communications and organizational skills with the ability to deal with ambiguity while juggling multiple priorities Experience with Agile methodology for team work and analytics 'product' creation. Experience in Reinforcement Learning is a plus. Experience in Simulation and Optimization problems in any space is a plus. Experience with Bayesian methods is a plus. Experience with Causal inference is a plus. Experience with NLP is a plus. Experience with working with FAIR data is a plus. Experience with Responsible AI is a plus. Experience with distributed machine learning is a plus Experience in DevOps, hands-on experience with one or more cloud service providers AWS, GCP, Azure(preferred) Model deployment experience is a must Experience with version control systems like GitHub and CI/CD tools Fluency in 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 Stakeholder engagement-BU, Vendors. Experience building statistical models in the Commercial, Net revenue Management or Supply chain space is a plusDifferentiating Competencies Required
Ability to work with virtual teams (remote work locations); lead team of technical resources (employees and contractors) based in multiple locations across geographies Lead 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