Job Classification:

As a Lead, Machine Learning Engineer, you will partner with Data Scientists, Data Engineers, Data Analysts and other professionals to implement machine learning models that will deliver stability, producibility, scalability and integration with other products and services. You will implement capabilities to solve sophisticated business problems, deploy innovative products, services and experiences to delight our customers! In addition to advanced technical expertise and experience, you will bring excellent problem solving, communication and teamwork skills, along with agile ways of working, strong business insight, an inclusive leadership attitude and a continuous learning focus to all that you do.

Here is what you can expect in a typical day:

• Operationalize ML software models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams; remove complex technicalimpediments
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
• Leverage cloud-based architectures and technologies to deliver optimized ML models at scale
• Construct optimized data pipelines to feed ML models
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
• Bring a strong understanding of relevant and emerging technologies, provide input and coach team members and embed learning and innovation in the day-to-day
• Work on complex problems in which analysis of situations or data requires an evaluation of intangible variables.
• Use programming languages including but not limited to Python, R, SQL, Java or Scala, SQL

The Skills and expertise you bring:

• Bachelor of Computer Science or Engineering or experience in related fields
• Ability to coach others with minimal guidance and effectively leverage diverse ideas, experiences, thoughts and perspectives to the benefit of the organization
• Experience with agile development methodologies and Test-Driven Development (TDD)
• Knowledge of business concepts, tools and processes that are needed for making sound decisions in the context of the company's business
• Ability to learn new skills and knowledge on an on-going basis through self-initiative and tackling challenges
• Excellent problem solving, communication and collaboration skills

Advanced experience and/or expertise with several of the following:

• Software Engineering & System Design: Requirement analysis, coding, and testing, version control, microservices architecture, building RestFul APIs, Distributed computing, architecture patterns, general understanding of computer architecture, Object-oriented programming concepts
• Machine Learning and Deep Learning: Good understanding of: ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc., Neural network, NLP, computer vision, and predictive analytics
• Model Performance Management: model monitoring, model validation, bias detection, explainability, performance, drift, outliers etc.
• Model Deployment: Thorough Understanding of MDLC (Model Development Life Cycle), CI/CD/CT pipelines (using tools like Jenkins, CloudBees, Harness etc.), A/B testing. Pipeline frameworks like MLFlow, AWS SageMaker pipeline etc. model and data versioning
• Data Integration, Transformation & Processing: Transforming and mapping raw data to generate insights. Data wrangling through various tools. Understanding big data ecosystems, relational, NOSQL and graph databases, unstructured and semi-structured data. Data processing on distributed systems with Spark/PySpark
• Statistics and Computing: Strong knowledge of: Linear Algebra, Probability and Statistics, Multivariate Calculus, Distributions like Poisson, Normal, Binomial etc.
• Programming Languages: Python, R, SQL, Java or Scala, SQL

• Market competitive base salaries, with a yearly bonus potential at every level.

• Medical, dental, vision, life insurance, disability insurance, Paid Time Off (PTO), and leave of absences, such as parental and military leave.

• 401(k) plan with company match (up to 4%).

• Company-funded pension plan.

• Wellness Programsincluding up to $1,600 a year for reimbursement of items purchased to support personal wellbeing needs.

• Work/Life Resources to help support topics such as parenting, housing, senior care, finances, pets, legal matters, education, emotional and mental health, and career development.

• Education Benefit to help finance traditional college enrollment toward obtaining an approved degree and many accredited certificate programs.

• Employee Stock Purchase Plan: Shares can be purchased at 85% of the lower of two prices (Beginning or End of the purchase period), after one year of service.

Eligibility to participate in a discretionary annual incentive program is subject to the rules governing the program, whereby an award, if any, depends on various factors including, without limitation, individual and organizational performance. To find out more about our Total Rewards package, visit Some of the above benefits may not apply to part-time employees scheduled to work less than 20 hours per week.