Data Scientist + ML Engineer

Job Description

DS Key Responsibilities Combine expertise in mathematics statistics computer science and domain knowledge to create AIML models to solve various business challenges Collaborate closely with the AI Technical Manager and GCC Petro technical professionals and data engineers to integrate models into the business framework Identify and frame opportunities to apply advanced analytics modeling and related technologies to data to help businesses gain insight and improve decision making workflow and automation Understand and communicate the value of proposed opportunity with team members and other stakeholders Identify needed data and appropriate technology to solve identified business challenges Clean data and develop and test models Establish the life cycle management process for models Provide technical mentoring in modeling and analytics technologies the specifics of the modeling process and general consulting skills Drive innovation in AIML to enhance capabilities in data driven decision making Aligns with team on shared goals and outcomes recognizes others contributions and work collaboratively seek diverse perspectives Takes actions to develop self and others beyond existing skillset Encourages innovative ideas adapts to change and changing technologies Understand and communicate data insights and model behaviors to stakeholders with varying levels of technical expertise Required Qualification Minimum 5 years of experience in designing and developing AIML models and or various optimization algorithms 5 to 9 years of experience Solid foundation in mathematics probability and statistics with demonstrated depth of knowledge and experience in advanced analytics and data science methodologies eg supervised and unsupervised learning statistics data science model development Proficiency in Python and working knowledge of cloud AIML services Azure Machine Learning and Databricks preferred Domain knowledge relevant to the energy sector and working knowledge of Oil and Gas value chain eg upstream midstream or downstream and associated business workflows Proven ability to frame data science opportunities leverage standard foundational tools and Azure services to perform exploratory data analysis for purposes of data cleaning and discovery visualize data and identify actions to reach needed results Ability to quickly assess current state and apply technical concepts across cross functional business workflows Experience with driving successful execution deliverables and accountabilities to meet quality and schedule goals Ability to translate complex data into actionable insights that drive business val Demonstrated ability to engage and establish collaborative relationships both inside and outside immediate workgroup at various organizational levels across functional and geographic boundaries to achieve desired outcomes Demonstrated ability to adjust behavior based on feedback and provide feedback to other Team oriented mindset with effective communication skills and the ability to work collaboratively Strong problem solving skills and attention to detail Excellent communication and collaboration skills ML Engineer Key Responsibilities Transform data science prototypes into appropriate scale solutions in a production environment Orchestrate and configure infrastructure that assists Data Scientists and analysts in building low latency scalable and resilient machine learning and optimization workloads into an enterprise software product Combine expertise in mathematics statistics computer science and domain knowledge to create advanced AIML models Collaborate closely with the AI Technical Manager and GCC Petro technical professionals and data engineers to integrate and scale models into the business framework Identify data appropriate technology and architectural design patterns to solve business challenges using approved standard analytical tools and AI design patterns and architectures Partner with Data Scientists and IT Foundational services to implement complex algorithms and models into enterprise scale machine learning pipelines Run machine learning experiments and finetune algorithms to ensure optimal performance Consistently deliver complex innovative and complete solutions driving them through design planning development and deployment that simplify business processes and workflows to drive business value Work collaboratively with a large variety of different teams including data scientists data engineers and solution architects from various organizations within business units and IT Required Qualification Minimum 2 years experience in Object Oriented Design andor Functional Programming in Python 2 to 5 years of experience Mature software engineering skills such as source control versioning requirement spec architecture and design review testing methodologies CICD etc Must have a disciplined methodical minimalist approach to designing and constructing layered software components that can be embedded within larger frameworks or applications Experience implementing machine learning frameworks and libraries such as MLflow Experience with containers and container managements docker Kubernetes Experience developing cloud first solutions using Microsoft Azure Services including building machine learning pipelines in Azure Machine Learning and or Fabric Hands on experience in deploying machine learning pipelines with Azure Machine Learning SDK Working knowledge of mathematics primarily linear algebra probability statistics and algorithms Proficient at orchestrating largescale MLDL jobs leveraging big data tooling and modern container orchestration infrastructure to tackle distributed training and massive parallel model executions on cloud infrastructure Experience designing custom APIs for machine learning models for training and inference processes and designing implementing and delivering frameworks for MLOps Experience with model lifecycle management and automation to support retraining and model monitoring Experience implementing and incorporating ML models on unstructured data using cognitive services and or computer vision as part of AI solutions and workflows History of working with large scale model optimization and hyperparameter tuning applied to MLDL models Knowledge of enterprise SaaS complexities including security access control scalability high availability concurrency online diagnoses deployment upgrade migration internationalization and production support Knowledge of data engineering and transformation tools and patterns such as Databricks Spark Azure Data Factory Ability to engage other technical experts at all organizational levels and assess opportunities to apply machine learning and analytics to improve business workflows and deliver information and insight to support business decisions Ability to communicate in a clear concise and understandable manner both orally and in writing