1

Data Scientist Forecasting Weekend Jobs in Texas

You find anomaly patterns others miss, propose forecasting solutions before anyone asks, and take ... Build scalable, maintainable data science solutions with clean architecture, disciplined testing ...

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Mentor data scientists and promote best practices in analytics and machine learning. * Support ...

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Mentor data scientists and promote best practices in analytics and machine learning. * Support ...

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Mentor data scientists and promote best practices in analytics and machine learning. * Support ...

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Mentor data scientists and promote best practices in analytics and machine learning. * Support ...

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Mentor data scientists and promote best practices in analytics and machine learning. * Support ...

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Mentor data scientists and promote best practices in analytics and machine learning. * Support ...

TX · On-site

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Mentor data scientists and promote best practices in analytics and machine learning. * Support ...

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Mentor data scientists and promote best practices in analytics and machine learning. * Support ...

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Mentor data scientists and promote best practices in analytics and machine learning. * Support ...

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Mentor data scientists and promote best practices in analytics and machine learning. * Support ...

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Mentor data scientists and promote best practices in analytics and machine learning. * Support ...

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Mentor data scientists and promote best practices in analytics and machine learning. * Support ...

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Mentor data scientists and promote best practices in analytics and machine learning. * Support ...

Experience in time-series forecasting, causal inference, feature engineering, and advanced data ... Mentor data scientists and promote best practices in analytics and machine learning. * Support ...

next page

Showing results 1-20

Data Scientist Forecasting Weekend information

What is a data scientist forecasting weekend?

Data Scientist Forecasting Weekend roles involve using statistical methods and machine learning techniques to predict trends, demands, or behaviors specifically for weekend periods. These professionals analyze large datasets to identify patterns that occur on weekends, such as sales fluctuations, customer activity, or resource needs. Their insights help businesses optimize staffing, inventory, and operations for better efficiency and profitability during weekends. Typically, they work with tools like Python, R, SQL, and specialized forecasting software. The role may require working weekends or providing analyses that inform weekend business strategies.

What are some common challenges data scientists specializing in forecasting face when working weekend shifts?

Data Scientists focused on forecasting during weekend shifts often encounter unique challenges such as limited access to key stakeholders for immediate clarifications, handling real-time data anomalies without full team support, and ensuring that time-sensitive predictions are delivered accurately under tight deadlines. Additionally, maintaining effective communication with cross-functional teams who may not be working at the same time can require proactive planning and thorough documentation. However, weekend shifts may also offer quieter work periods for deep analysis and model refinement, allowing for focused progress on complex forecasting tasks.

What are the key skills and qualifications needed to thrive as a data scientist specializing in forecasting on weekends, and why are they important?

To thrive as a Data Scientist in Forecasting, you need expertise in statistical modeling, time series analysis, and a strong background in mathematics or computer science, often supported by a relevant degree. Familiarity with programming languages like Python or R, experience with machine learning libraries (such as scikit-learn or TensorFlow), and proficiency in data visualization tools are crucial. Analytical thinking, problem-solving, and effective communication are important soft skills to interpret data and present actionable insights to stakeholders. These skills ensure accurate predictions, drive data-driven decisions, and support organizational goals in dynamic business environments.

What is the difference between Data Scientist Forecasting Weekend vs Data Analyst Forecasting Weekend?

AspectData Scientist Forecasting WeekendData Analyst Forecasting Weekend
Required CredentialsBachelor's or Master's in Data Science, Statistics, or related field; often some experience with machine learningBachelor's in Data Analysis, Statistics, or related field; focus on data interpretation and reporting
Work EnvironmentCollaborative teams, often in tech or finance industries, working on predictive modelsBusiness units, focusing on data reporting, visualization, and basic analysis
Employer & Industry UsageTech companies, finance, e-commerce, and consulting firmsRetail, marketing, healthcare, and finance sectors

Data Scientist Forecasting Weekend roles typically require advanced statistical and machine learning skills, working on predictive models. Data Analyst Forecasting Weekend positions focus on data interpretation, reporting, and visualization. Both roles involve forecasting but differ in complexity and technical depth.

What are the most commonly searched types of Data Scientist Forecasting jobs in Texas?

The most popular types of Data Scientist Forecasting jobs in Texas are:

Sr. Data Scientist (Forecasting)

Nestlé S.A.

Arlington, TX • On-site

$110 - $160/hr

Other

Retirement

Posted 15 days ago


Job description

As the digital arm for the world's largest food and beverage company, Nestlé IT & Digital Americas harnesses the power of data, analytics, and innovative technology to deliver transformative solutions and business resiliency for Nestlé businesses and iconic brands from Purina® to Nescafé®.We’re innovators, strategic collaborators, value multipliers, and digital business leaders committed to delivering results that create meaningful experiences and drive value, enabling Nestlé to win in the marketplace. By joining, you become part of a 150-year legacy and a global team of 270,000+. We invest in our people—their growth, development, and sense of belonging. This shared unity and purpose is what motivates individuals to join, stay, and advance their careers within Nestlé.

This position is not eligible for Visa Sponsorship.

Position Summary

Join Nestlé IT & Digital as a Sr. Data Scientist supporting Nestlé USA Digital, whereyou'llpartner with the Supply Chain Planning team and the Analytics team to build the forecasting solutions that power how Nestlé USA plans andoperatesat scale.You'lldesign and deploy production-grade forecasting models, embed AI into how forecasts get delivered, and serve as a key liaison between the technical and functional sides of Enterprise Forecasting.You'llbring a strong AI mindset, curiosity about emerging technologies, and comfortoperatingin ambiguity as the forecasting landscape evolves.

  • Design, develop, test, and deploy statistical and machine learning forecasting models in production on cloud-based analytics platforms, with a focus on demand pattern recognition, algorithm selection, outlier correction, and parameter optimization to drive forecast accuracy and bias reduction
  • Build andmaintainstructured, programmatic forecasting processes that deliver at scale,leveragingML Ops practices to automate weekly forecast generation and reduce manual effort
  • Embed Agentic AI and GenAI capabilities into forecasting workflows toidentifyaccuracy improvement opportunities, automate root cause analysis, and support insights generation and decision-making
  • Continuously explore and apply emerging AI/ML techniques such as deep learning and probabilistic forecasting to improve forecast accuracy and scalability across a complex portfolio of SKUs, clusters, and data sources
  • Serve as a liaison between technical and functional teams, partnering with Supply Chain Planning leads and cross-functional stakeholders across Marketing, Finance, and Sales to align business needs with system and process solutions
  • Advance Enterprise Forecasting as part of the One Planning initiative and support Customer Order Fulfilment improvements, enabling standardized, scalable, and cross-functional forecasting solutions across the organization
  • Deliver training and forecast office hours to Supply Chain Planning users to build analytics literacy, promote adoption, and increase enterprise value
  • Create and enhance Power BI reports for forecast KPI monitoring and data validation, and present findings through data visualization and PowerPoint tailored to the audience
Requirements
  • Bachelor's degree in statistics, mathematics, economics, business, or a related field
  • 5+ years of experience in data science, forecasting, or advanced analytics, with hands-on experience in SQL, Python, or a similar language
  • 5+ years of experience extracting, transforming, and analyzing large datasets, and creating data visualizations to communicate insights
  • 3+ years of experience deploying andmaintainingmachine learning models in production, including regression, classification, time series modeling, and feature engineering
Other
  • Master's degree in statistics, mathematics, economics, business, or a related discipline is preferred
  • Experience with ML Ops practices,cloudand AI/ML platforms (Databricks, Snowflake, Microsoft Azure), GenAI solutions, and Microsoft Power BI is preferred
  • Experience supporting enterprise-scale transformation or Supply Chain planning initiatives in an Agile environment is preferred

Don't meet all the qualifications listed under "other"? These are preferred, but not required. When you apply for a role with Nestlé, we ensure that individual confidentiality is held to the highest regard. We are intentional about creating an inclusive workplace for everyone. We consider our associates our most valuable assets.

The approximate pay range for this position is$110,000 to $160,000per year. Please note that the pay range provided is a good faith estimate for the position at the time of posting. Final compensation may vary based on factors including but not limited to knowledge,skillsand abilities as well as geographic location. Nestlé offers performance-based incentives and a competitive total rewards package, which includes a 401k with companymatch, healthcarecoverageand a broad range of other benefits. Incentives and/or benefit packages may vary depending on the position. Learn more athttps://nestlejobs.com/nestle-in-the-us .

It is our

#J-18808-Ljbffr