ROLE: DATA ENGINEER - SQL SERVER METRIC MIGRATION Location: Omaha NE or Dallas TX (Day 1 Onsite) SUMMARY: DATA ENGINEERING TO MOVE SQL SERVER TABLES AND SSIS JOBS TO TERADATA OR SNOWFLAKE. MUST HAVE: EXPERIENCE WITH SSIS…
Data Scientist jobs in Omaha, NE
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
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career in 2024, have a passion for working with numbers and accounts, detail orientation and enjoy working on the computer and analyzing data, we would love to talk with you. We will develop and train you to learn our sy…
A leading construction and engineering organization is seeking a Microsoft AI Engineer to help expand and scale enterprise AI capabilities across critical business functions. This role will focus on designing, deploying,…
Job Description Job Description We are looking for a detail-oriented Data Analyst to support a long-term contract assignment within the health pharm/biotech industry in Omaha, Nebraska. In this role, you will help mainta…
Position: Cloud Data Engineer Mid-level Location: Omaha, NE Job type: Contract MUST HAVE: SNOWFLAKE - STRONG HANDS-ON EXPERIENCE WITH SNOWFLAKE: SQL DEVELOPMENT, WAREHOUSING CONCEPTS, AND WORKLOAD MANAGEMENT PYTHON - SPE…
Electrical Engineer in our Advanced Manufacturing group, you’ll contribute to projects that enable the heart of our clients’ successful data center facilities engineering, design and construction. In this role, you will…
The Data Services Analyst delivers custom data solutions that help DMSi customers maximize the value from their ERP solution. This role works across a broad and growing set of technologies, including custom reporting, bu…
Data Scientist - AI Systems This role focuses on designing, building, and supporting the deployment of AI-powered systems that solve business problems from intelligent agents and conversational interfaces to analytical w…
of a global team of over 50,000 planners, designers, engineers, scientists, digital innovators, program and construction managers and... .... Job Description AECOM is seeking a Control Room Data Analyst - Auditor to supp…
multiple states, and we think you’ll agree that YOU BELONG AT BAXTER SHARED SERVICES! Your Purpose as a Business Intelligence & Data Analyst: The Business Intelligence & Data Analyst sits at the intersection of business…
and Kanban ~ Experience with technology testing methods and procedures ~ Proficiency with personal computers, spreadsheets, and data manipulation tools ~ Ability to write and decompose user stories, test cases, and accep…
Calling all innovators – find your future at Fiserv. We’re Fiserv, a global leader in Fintech and payments, and we move money and information in a way that moves the world. We connect financial institutions, corporations…
What data scientists earn in Omaha
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $36–$49 | $74k–$101k |
| Mid level | $49–$66 | $101k–$138k |
| Senior | $64–$88 | $133k–$184k |
Adjusted for the Omaha market from national ranges.
What employers ask for
The skills these listings keep naming
Interview questions worth rehearsing
With the thing the interviewer is actually listening for
Walk me through a data project that changed a decision.
Structure it as question, approach, finding, action. Projects that ended in a decision — even 'we did not launch' — beat technically impressive analyses that went nowhere.
How would you design an A/B test for a new feature?
Cover the metric, randomization unit, sample size, and duration, plus a pitfall like peeking or interference. Practical rigor is what is being tested.
Your model performs well offline but poorly in production. Why might that be?
Discuss train/serve skew, data leakage, distribution shift, and feedback loops. Listing several plausible causes and how you would check each is the strong answer.
Explain p-values or confidence intervals to a non-technical stakeholder.
Use plain language and a concrete scenario, and resist overstating certainty. They are testing whether your statistics survive translation.
How do you handle missing or messy data?
First ask why it is missing — the mechanism matters more than the imputation method. Then discuss options and how the choice affects conclusions.
When would you not use machine learning for a problem?
When a rule, a query, or a simple heuristic wins on cost and interpretability. Knowing when ML is overkill signals maturity.
How do you decide which metric a team should optimize?
Talk about proxy versus true goals, gameability, and counter-metrics. A story about a metric that backfired is very effective here.
Resume tips that move the needle
For data scientists specifically — generic advice costs you here
Lead every bullet with the business result — revenue, retention, cost — and put the method second.
Specify your stack concretely: Python libraries, SQL dialects, experimentation platforms, and any production ML experience.
Distinguish shipped work from research; 'model serving 2M users' and 'notebook analysis' are different claims.
Keep one or two public projects or publications linkable, tailored to the industry you are targeting.
Name the size and kind of data you worked with — event streams, tabular, text — so teams can map you to their problems.
Where this role goes
Typical progression
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