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Data Scientist Startup Jobs in Texas (NOW HIRING)

Gold Standard Auctions

Data Scientist

Dallas, TX · On-site

$20 - $28/hr

Comfortable in a startup environment where priorities change quickly * Strong problem solver who ... data science, analytics, or similar role * Strong proficiency in Python (pandas, numpy, data ...

Shipwell

Senior Data Scientist

Austin, TX · On-site

With a fast-paced and gritty startup mentally, which is generating a buzz in the investment world ... DATA SCIENTIST The Data Scientist role provides you with a unique opportunity to join a high growth ...

Shipwell

Senior Data Scientist

Austin, TX

With a fast-paced and gritty startup mentally, which is generating a buzz in the investment world ... DATA SCIENTIST The Data Scientist roleprovides you with a unique opportunity to join a high growth ...

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All JobsData Scientist Startup JobsData Scientist Startup Jobs in Texas

Data Scientist Startup information

See Texas salary details

$34.9K

$114.3K

$183.1K

How much do data scientist startup jobs pay per year?

As of Aug 30, 2026, the average yearly pay for data scientist startup in Texas is $114,350.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,800.00 and $126,700.00 per year, depending on experience, location, and employer.

What does a data scientist do at a startup?

A Data Scientist at a startup typically wears many hats, leveraging data to drive business decisions, build predictive models, and develop data-driven products. Unlike in larger companies, startup Data Scientists often work closely with other teams, handle end-to-end data pipelines, and may even take on responsibilities like data engineering or analytics. The fast-paced environment requires adaptability, strong problem-solving skills, and the ability to quickly turn raw data into actionable insights that can have a direct impact on the company's growth.

What are the key skills and qualifications needed to thrive as a data scientist in a startup?

To thrive as a Data Scientist in a startup, you need a solid foundation in statistics, data analysis, and programming (often Python or R), usually backed by a relevant degree in computer science, mathematics, or a related field. Familiarity with machine learning libraries (like scikit-learn, TensorFlow), cloud platforms (such as AWS or GCP), and data visualization tools (such as Tableau or Power BI) is typically required. Strong problem-solving skills, adaptability, and the ability to communicate complex findings to non-technical stakeholders are essential soft skills. These competencies are crucial in a startup environment, where rapid iteration, cross-functional collaboration, and translating data insights into actionable business strategies are key to driving growth and innovation.

What unique challenges might a data scientist face when working at a startup compared to a larger company?

At a startup, Data Scientists often work with limited resources, less historical data, and rapidly changing business priorities. This means you'll likely wear multiple hats—handling data collection, cleaning, modeling, and sometimes even deploying solutions on your own. The pace is fast, and you'll need to adapt quickly to shifting objectives while collaborating closely with engineers, product managers, and founders. However, this environment also offers significant opportunities to make a direct impact and gain broad experience across the data science pipeline.

What is the difference between Data Scientist Startup vs Data Analyst?

AspectData Scientist StartupData Analyst
Required CredentialsBachelor's or Master's in Data Science, Statistics, or related fields; programming skills in Python/RBachelor's in Statistics, Mathematics, or related fields; proficiency in Excel, SQL
Work EnvironmentFast-paced startup setting, cross-functional teams, innovative projectsCorporate or business environment, reporting and visualization tasks
Employer & Industry UsageTech startups, e-commerce, SaaS companiesFinancial firms, marketing agencies, retail companies

In summary, Data Scientist Startup roles focus on advanced analytics, machine learning, and building predictive models, requiring stronger technical skills and often a higher level of education. Data Analysts typically handle data reporting, visualization, and basic analysis, with a focus on interpreting existing data for business insights. Both roles are essential but differ in complexity and scope within startup environments.

What are popular job titles related to Data Scientist Startup jobs in Texas?

For Data Scientist Startup jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Data Scientist Startup jobs in Texas look for?

The top searched job categories for Data Scientist Startup jobs in Texas are:

What cities in Texas are hiring for Data Scientist Startup jobs?

Cities in Texas with the most Data Scientist Startup job openings:

Data Scientist Startup jobs near you
Infographic showing various Data Scientist Startup job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 11% Part Time, 4% Contract, and 1% Nights. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $114,350 per year, or $55 per hour.

Data Scientist

Dallas, TX • On-site

Gold Standard Auctions
See more jobs at Gold Standard Auctionsexternal

$20 - $28/hr

Full-time

Medical, Dental, Vision, PTO

Re-posted 16 days ago

Job description

We are a fast-growing auction and e-commerce company specializing in coins and precious metals, operating at high volume across multiple sales channels. We move quickly, make decisions off real data, and are building the systems to scale into a much larger operation.


Position Overview

We are looking for a high-impact Data Scientist who can turn messy, real-world data into clear insights and actionable dashboards. This is not a “sit back and analyze” role—you will be expected to build, automate, and drive decisions across the business.

You’ll work directly with leadership to identify problems, clean and structure data, and create tools that improve how we buy, price, sell, and operate.


What You’ll Do

  • Build and maintain dashboards to track key business metrics (sales, margins, inventory, performance)
  • Clean, structure, and unify data from multiple sources (CSV exports, APIs, internal systems)
  • Automate recurring workflows (reporting, pricing updates, inventory tracking, alerts)
  • Analyze large datasets to identify trends, inefficiencies, and opportunities
  • Partner with operations, sales, and leadership to drive data-backed decisions
  • Create simple, scalable tools that non-technical team members can use
  • Work on ad hoc, high-priority problems with shifting requirements
  • Continuously improve data pipelines and reporting accuracy


Who We’re Looking For

  • A go-getter who takes ownership and figures things out without needing constant direction
  • Comfortable in a startup environment where priorities change quickly
  • Strong problem solver who can move from messy data → clear insight → action
  • Able to balance speed and accuracy in a high-growth environment
  • Strong communicator who can explain data to non-technical stakeholders


Qualifications

  • 2+ years of experience in data science, analytics, or similar role
  • Strong proficiency in Python (pandas, numpy, data processing)
  • Strong SQL skills for querying and transforming data
  • Experience building dashboards (Tableau, Power BI, Looker, or similar)
  • Experience working with messy, unstructured data
  • Familiarity with APIs and data integration
  • Experience automating workflows and processes


Preferred Skills

  • Experience in e-commerce, marketplaces, or high-volume transaction environments
  • Familiarity with forecasting, pricing models, or optimization problems
  • Experience with cloud platforms (especially Microsoft Azure)
  • Experience with scripting/automation (cron jobs, pipelines, etc.)

Company Description

Gold Standard Auctions is a rapidly growing auction house specializing in coins, precious metals, and collectibles. We handle high volumes of inbound interest from buyers and sellers and are building scalable teams to support our growth.