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Machine Learning Manager Jobs in Sidney, NE (NOW HIRING)

Senior Staff, Data Engineer

Sterling, CO

$96K - $131K/yr

Experience serving machine learning models at scale, with attention to latency and monitoring. * Experience with Customer 360, customer data platforms, master data management, deterministic or ...

Solution Architect

Sterling, CO

$57.50 - $75.75/hr

Identify opportunities to leverage Artificial Intelligence (AI), Machine Learning (ML), automation ... model lifecycle management, regulatory compliance, and ethical AI practices. * Application ...

Machine Learning Manager information

See Sidney, NE salary details

$52K

$83.3K

$120.3K

How much do machine learning manager jobs pay per year?

As of Sep 2, 2026, the average yearly pay for machine learning manager in Sidney, NE is $83,270.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,300.00 and $94,300.00 per year, depending on experience, location, and employer.

What is a machine learning manager?

Machine Learning Managers are professionals responsible for leading teams that develop, implement, and maintain machine learning models and systems. They oversee data scientists, engineers, and other specialists, ensuring projects align with business goals and are delivered on time. Their role often involves coordinating cross-functional teams, managing project timelines, and staying current with the latest advancements in artificial intelligence and machine learning. Additionally, they may be involved in hiring, mentoring, and providing technical guidance to their team.

What are the key skills and qualifications needed to thrive as a machine learning manager?

To thrive as a Machine Learning Manager, you need a robust background in machine learning algorithms, statistical analysis, and software engineering, typically supported by an advanced degree in computer science or a related field. Familiarity with tools such as Python, TensorFlow, PyTorch, and project management platforms, along with experience in deploying ML systems, is essential. Strong leadership, communication, and strategic thinking skills set exceptional managers apart, enabling them to guide teams and align projects with business objectives. These skills are crucial to successfully leading technical teams, ensuring project delivery, and translating complex ML solutions into organizational value.

What are some of the main challenges a machine learning manager faces when leading a team?

A Machine Learning Manager often navigates challenges such as balancing project deadlines with the need for thorough experimentation and research, ensuring clear communication between technical and non-technical stakeholders, and fostering collaboration among data scientists, engineers, and product teams. Additionally, managers must keep their team's skills current with rapidly evolving technologies while also addressing issues like data quality and model deployment in production environments. Successfully overcoming these challenges requires strong leadership, adaptability, and a deep understanding of both business objectives and technical intricacies.

Is machine learning a high paying job?

Machine Learning Managers typically earn high salaries due to their specialized skills in data analysis, programming, and model development. Compensation varies based on experience, location, and industry, but it is generally considered a well-paying role within the tech sector.

Senior Staff, Data Engineer

Asurion

Sterling, CO

$96K - $131K/yr

Full-time

Re-posted 24 days ago


Asurion rating

7.2

Company rating: 7.2 out of 10

Based on 84 frontline employees who took The Breakroom Quiz

137th of 226 rated it services


Job description

Senior Staff, Data Engineer

Team: Customer 360 Platform
Location: Sterling, VA
Focus: Python, SQL, Spark, applied statistics, machine learning, streaming, APIs

About the Role

Asurion is hiring a Senior Staff, Data Engineer for the Customer 360 Platform team. Customer 360 is a strategic platform that creates a trusted, intelligent view of our customers across products, partners, subscriptions, claims, service interactions, and digital experiences.

This is a data engineering role with a strong data science foundation, on a team whose product is analytical data serving. The work goes beyond moving and transforming data: you will own the pipelines, statistical validation, models, and APIs that turn customer data into reliable metrics and predictions used in real business decisions, and you will serve those results to consuming applications through well-designed APIs and events.

The role aligns to Asurion's Senior Staff expectations for cross-team API design, domain data architecture, systems design, and technical leadership.

What You Will Do

  • Own analytical data products end to end, from data model and pipeline through to the metric, model, or API that a product or business decision depends on.
  • Apply statistical methods in production work: experiment design and A/B testing, sample sizing and confidence intervals, significance testing, customer segmentation, and validation beyond basic null checks, including distribution and drift checks, anomaly detection, reconciliation, and outlier handling.
  • Build, evaluate, and operate classical machine learning models in production (for example regression, gradient boosting, clustering, time series, and anomaly detection), including feature engineering, evaluation with metrics such as precision, recall, AUC, and MAPE, and monitoring for drift.
  • Design and operate APIs, events, and data contracts that serve customer data to internal consumers, with clear schemas, versioning, and reliable latency.
  • Build streaming and event-driven data flows using technologies such as Kafka, Kinesis, CDC, and Spark Structured Streaming to support near-real-time and operational analytics.
  • Design customer data models for CustomerID, HouseholdID, profiles, subscriptions, interactions, relationships, identity resolution, lineage, confidence, and data quality, working across relational, NoSQL, graph, cache, search, and event-driven patterns.
  • Partner with product, architecture, data, security, privacy, and analytics teams to drive scalable adoption of the platform.
  • Mentor engineers and raise engineering and analytical standards across teams.

Required Qualifications

  • 10+ years of experience building APIs, backend services, and data-intensive platforms, with a track record of delivering measurable business or analytical outcomes such as attribution, fraud detection, churn reduction, conversion, or targeting.
  • Solid grounding in applied statistics: sampling and sample sizing, confidence intervals, hypothesis testing, base rates, sources of bias in data, and evaluation of model quality.
  • Hands-on experience taking classical machine learning models into production and owning them end to end, from features and training data through evaluation, serving, and drift monitoring.
  • Strong Python and SQL, with production experience in Apache Spark.
  • Experience building and operating APIs or backend services for data consumers, including API design, schema evolution, and production service architecture.
  • Experience with event-driven architecture, data contracts, and streaming or messaging platforms such as Kafka, Kinesis, or Flink.
  • Deep understanding of data modeling, database design, schema evolution, identity resolution, data quality, and source-of-truth patterns, across databases such as PostgreSQL, MySQL, DynamoDB, MongoDB, Redis, Elasticsearch/OpenSearch, Neo4j, or similar.
  • Ability to influence multiple teams, shape technical direction, and drive cross-functional outcomes.

Preferred Qualifications

  • Experience with Databricks, Delta Lake, Unity Catalog, or Delta Live Tables.
  • Experience with Node.js and TypeScript service development.
  • Experience serving machine learning models at scale, with attention to latency and monitoring.
  • Experience with Customer 360, customer data platforms, master data management, deterministic or probabilistic matching, confidence scoring, or graph-based models.
  • Experience working in privacy-sensitive, regulated, multi-tenant, or client-segregated data environments.

Is This Role Right for You?

This role is a strong fit if you have built data platforms and also done the analysis: you think about whether a number can be trusted before you ship it, and your work carries through to the decision it informed. It is less of a fit if your recent experience is primarily platform operations (cluster administration, CI/CD, and pipeline monitoring) or generative AI application work without a foundation in classical statistics and modeling.

Why Join Customer 360

Customer 360 is a high-impact platform where customer data, identity, confidence, privacy, statistics, and API design all matter. As a Senior Staff, Data Engineer, you will help define the data foundation, analytical standards, and serving layer for a platform that powers intelligence across Asurion.

Equal Opportunity

Asurion is proud to be an equal opportunity employer committed to building a diverse and inclusive workplace.


What Asurion employees say

Pay

Benefits

Hours and flexibility

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About Asurion

Sourced by ZipRecruiter

As the world's leading tech care company, Asurion eliminates the fears and frustrations associated with technology, to ensure our 300 million customers get the most out of their devices, appliances and connections. We provide insurance, repair, replacement, installation and 24/7 support for everything from cellphones to laptops and household appliances. Our experts are available online, on the phone, at one of our more than 700 stores, or can even come to you.

Industry

It services

Company size

10,000+ Employees

Headquarters location

Nashville, TN, US

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