Job Description
Role & responsibilities
Promotes learning in others by proactively providing and/or developing information, resources, advice, and expertise
with coworkers and members; builds relationships with cross-functional/external stakeholders and customers. Listens
to, seeks, and addresses performance feedback; proactively provides actionable feedback to others and to managers.
Pursues self-development; creates and executes plans to capitalize on strengths and develop weaknesses; leads by
influencing others through technical explanations and examples and provides options and recommendations. Adopts
new responsibilities; adapts to and learns from change, challenges, and feedback; demonstrates flexibility in
approaches to work; champions change and helps others adapt to new tasks and processes. Facilitates team
collaboration to support a business outcome.
• Completes work assignments autonomously and supports business-specific projects by applying expertise in subject
area and business knowledge to generate creative solutions; encourages team members to adapt to and follow all
procedures and policies. Collaborates cross-functionally and/or externally to achieve effective business decisions;
provides recommendations and solves complex problems; escalates high-priority issues or risks, as appropriate;
monitors progress and results. Supports the development of work plans to meet business priorities and deadlines;
identifies resources to accomplish priorities and deadlines. Identifies, speaks up, and capitalizes on improvement
opportunities across teams; uses influence to guide others and engages stakeholders to achieve appropriate solutions.
• Develops detailed problem statements outlining hypotheses and their effect on target clients/customers by defining
scope, objectives, outcome statements and metrics.
• Designs and develops data pipelines and automation for data acquisition and ingestion of raw data from multiple data
sources and data formats by transforming, cleansing, and storing data for consumption by downstream processes;
writing and optimizing diverse SQL queries; and demonstrating advanced knowledge of database fundamentals.
• Analyzes and investigates complex data sets and summarizes key characteristics by employing data visualization
methods; and determining how best to manipulate data sources to discover patterns, spot anomalies, test hypotheses,
and/or check assumptions.
• Selects, manipulates, and transforms data into features used in machine learning algorithms by leveraging techniques
to conduct dimensionality reduction, feature importance, and feature selection.
• Trains statistical models by using algorithms and data mining techniques; testing models with various algorithms to
assess the input dataset and related features; and applying techniques to prevent overfitting such as cross-validation.
• Deploys and maintains reliable and efficient models through production.
• Verifies model performance by demonstrating expertise in the practice of a variety of model validation techniques to
assess and discriminate the goodness of model fit; and leveraging feedback and output to manage and strengthen
model performance.
• Collaborates with internal and external stakeholders across domains to develop and deliver statistical driven outcomes
by delivering insights and values from heterogeneous data to investigate complex problems for multiple use cases;
driving informed decision-making; and presenting findings to both technical and non-technical audiences.
Preferred candidate profile
MLOps and AI platform exposure (model deployment/monitoring, tools like SageMaker, Vertex AI, MLflow).
SRE practices (SLIs/SLOs, error budgets, incident management, runbooks, postmortems).
IT operations domain and soft skills (ITIL/ITSM, ticketing tools, clear communication, structured problem solving).