Data Scientist – Forecasting and Experiment Analysis

The Elite Job; Delphos (OH); Hybrid; USD 90,000 - 150,000; Full time

Benefits offered by this job

Flexible Working Options

Professional Development

Learning Opportunities

Career Growth

Collaborative Environment

Meaningful Projects

Performance Recognition

Work-Life Balance

Job summary

The Elite Job in Ohio is seeking a Data Scientist – Forecasting and Experiment Analysis to join its expanding data and analytics team. The role focuses on building forecasting models, designing controlled experiments, and turning data into actionable insights for business decisions.

You will work with Python, SQL, and modern analytics tools, collaborating with stakeholders and communicating results clearly to both technical and non-technical audiences.

Qualifications

• A bachelors degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative discipline is preferred.
• A masters degree in Data Science, Statistics, Applied Mathematics, Machine Learning, Econometrics, or a related field is an advantage.
• Equivalent practical experience, relevant projects, and demonstrated technical skills may be considered where appropriate.
• Relevant certifications or coursework in statistical analysis, machine learning, forecasting, or experimentation are beneficial.

Responsibilities

• Develop, implement, and maintain forecasting models to predict business performance, customer demand, sales trends, operational requirements, and other relevant business metrics.
• Apply statistical forecasting methods, time-series analysis, regression techniques, and machine learning algorithms to identify patterns and estimate future outcomes.
• Analyze historical data, seasonal variations, market trends, and external factors that may influence forecasting accuracy.
• Evaluate different forecasting approaches and select suitable models based on business objectives, data availability, and performance requirements.
• Monitor model accuracy over time and update forecasting methodologies when new data, changing business conditions, or performance issues arise.
• Build forecasting solutions that support planning, budgeting, inventory management, resource allocation, customer behavior analysis, and strategic decision-making, where applicable.

Job description

1. Job Summary

The Elite Job is seeking a skilled, analytical, and results-oriented Data Scientist – Forecasting and Experiment Analysis to join its growing data and analytics team. This role is ideal for a professional who enjoys transforming complex datasets into meaningful insights, developing reliable forecasting models, and evaluating experiments to support data-driven business decisions.

The successful candidate will work with structured and unstructured data, statistical methods, machine learning algorithms, and predictive analytics techniques to identify trends, forecast future outcomes, and measure the effectiveness of business strategies. The role involves collaborating with business stakeholders, data engineers, product teams, and other analytical professionals to understand business challenges and develop practical, evidence-based solutions.

The Data Scientist will play an important role in building forecasting models, designing and analyzing controlled experiments, interpreting statistical results, and communicating findings to technical and non-technical audiences. The position requires a combination of programming expertise, statistical knowledge, critical thinking, and business understanding.

Candidates who are passionate about data science, predictive modeling, experimentation, and continuous learning are encouraged to apply. Applicants with relevant academic knowledge, practical project experience, or professional experience in data analytics and machine learning will be considered according to the requirements of the position.

2. Key Responsibilities

Forecasting and Predictive Modeling

Develop, implement, and maintain forecasting models to predict business performance, customer demand, sales trends, operational requirements, and other relevant business metrics.

Apply statistical forecasting methods, time-series analysis, regression techniques, and machine learning algorithms to identify patterns and estimate future outcomes.

Analyze historical data, seasonal variations, market trends, and external factors that may influence forecasting accuracy.

Evaluate different forecasting approaches and select suitable models based on business objectives, data availability, and performance requirements.

Monitor model accuracy over time and update forecasting methodologies when new data, changing business conditions, or performance issues arise.

Build forecasting solutions that support planning, budgeting, inventory management, resource allocation, customer behavior analysis, and strategic decision-making, where applicable.

Experiment Design and Analysis

Design and analyze controlled experiments, including A/B tests, to evaluate the effectiveness of product changes, marketing campaigns, operational improvements, and business initiatives.

Define measurable objectives, primary and secondary metrics, hypotheses, and appropriate evaluation methods before experiments begin.

Apply statistical techniques to estimate treatment effects, assess uncertainty, and determine whether observed differences are meaningful.

Conduct power analysis and sample-size calculations to support reliable experimental design.

Identify potential sources of bias, confounding variables, selection effects, and data quality issues that could affect experimental conclusions.

Interpret experimental results and translate findings into actionable recommendations for business stakeholders.

Help teams establish consistent experimentation practices and document lessons learned from completed tests.

Data Collection and Preparation

Collect, clean, validate, and transform data from databases, data warehouses, APIs, business applications, and other approved sources.

Perform exploratory data analysis to understand data distributions, relationships, anomalies, missing values, and potential forecasting or experimentation opportunities.

Develop reusable data-processing workflows using Python, SQL, and relevant analytical tools.

Collaborate with data engineers to improve data availability, reliability, consistency, and suitability for statistical analysis.

Establish appropriate data validation checks to reduce errors and improve the reliability of analytical outputs.

Maintain clear documentation of data sources, assumptions, analytical methods, and model limitations.

Statistical Analysis and Model Evaluation

Apply statistical inference, probability theory, regression analysis, hypothesis testing, and other quantitative methods to solve business problems.

Select suitable evaluation metrics for forecasting and predictive models, such as MAE, RMSE, and MAPE, where appropriate.

Compare model performance against baseline methods and communicate the practical significance of improvements.

Investigate model errors, unexpected results, and performance variations to identify opportunities for improvement.

Evaluate model stability, reliability, and suitability for the intended business application.

Use validation techniques that respect the time-dependent nature of forecasting data and help prevent data leakage.

Communicate uncertainty and confidence intervals appropriately rather than presenting estimates as guaranteed outcomes.

Business Insights and Reporting

Translate complex analytical findings into clear, understandable insights for technical and non-technical stakeholders.

Prepare reports, dashboards, presentations, and visualizations that explain forecasts, experiment results, trends, and recommendations.

Work with business teams to identify key performance indicators and define measurable success criteria.

Present analytical results in a way that supports informed decisions, prioritization, and resource planning.

Explain model assumptions, limitations, risks, and confidence levels to help stakeholders understand the implications of the findings.

Recommend evidence-based improvements to business processes, customer experiences, and operational strategies.

Collaboration and Continuous Improvement

Partner with data engineers, software engineers, product managers, business analysts, and other stakeholders to deliver analytical solutions.

Participate in project planning, requirement discussions, technical reviews, and knowledge-sharing sessions.

Maintain reproducible analytical workflows through version control, testing, documentation, and good coding practices.

Explore relevant statistical methods, forecasting techniques, and machine learning tools to improve analytical effectiveness.

Support the responsible use of data by following applicable privacy, security, governance, and ethical standards.

Contribute to the development of reusable analytical frameworks, model evaluation processes, and experimentation guidelines.

3. Required Skills and Qualifications

Educational Qualifications

• A bachelors degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative discipline is preferred.
• A masters degree in Data Science, Statistics, Applied Mathematics, Machine Learning, Econometrics, or a related field is an advantage.
• Equivalent practical experience, relevant projects, and demonstrated technical skills may be considered where appropriate.
• Relevant certifications or coursework in statistical analysis, machine learning, forecasting, or experimentation are beneficial.

Technical Skills

• Strong working knowledge of Python for data analysis, statistical modeling, and predictive analytics.
• Proficiency in SQL for querying, extracting, joining, and transforming data from relational databases.
• Understanding of statistical concepts, including probability distributions, hypothesis testing, confidence intervals, regression analysis, and statistical significance.
• Experience with forecasting approaches such as time-series decomposition, exponential smoothing, ARIMA, or other suitable forecasting methods.
• Familiarity with machine learning techniques, feature engineering, model training, validation, and performance evaluation.
• Knowledge of data manipulation and analytical libraries such as pandas, NumPy, SciPy, and scikit-learn.
• Ability to create clear visualizations using Matplotlib, Seaborn, Plotly, Power BI, Tableau, or similar tools.
• Understanding of experimental design, A/B testing, sample-size estimation, statistical power, and treatment-effect analysis.
• Familiarity with Jupyter Notebook, Git, and reproducible analytical workflows.
• Knowledge of cloud data platforms, data warehouses, and large-scale data processing tools is an advantage.

Analytical and Problem-Solving Skills

• Strong quantitative reasoning and the ability to approach complex problems systematically.
• Ability to identify meaningful trends, relationships, anomalies, and patterns in large datasets.
• Confidence in selecting appropriate statistical methods based on the business question and characteristics of the data.
• Ability to distinguish correlation from causation and recognize the limitations of observational data.
• Attention to detail when validating data, interpreting model outputs, and assessing experimental results.
• Ability to evaluate competing analytical approaches and explain why a particular method is suitable.
• A practical mindset focused on generating reliable insights and measurable business value.

Communication and Collaboration Skills

• Strong written and verbal communication skills.
• Ability to explain statistical concepts and technical findings in accessible language.
• Confidence in presenting recommendations, analytical reports, and experiment outcomes to stakeholders.
• Effective collaboration with multidisciplinary teams.
• Strong organizational skills and the ability to manage multiple analytical tasks.
• Willingness to receive feedback, share knowledge, and contribute to team learning.

4. Experience

The required level of experience will depend on the scope and seniority of the position.

Entry-Level Candidates:

Applicants may qualify through relevant academic projects, internships, research assignments, or independent projects demonstrating proficiency in Python, SQL, statistics, and predictive modeling.

Early-Career Professionals:

Candidates with approximately 1–3 years of relevant experience in data science, forecasting, business analytics, statistical analysis, or machine learning are encouraged to apply.

Experienced Professionals:

Candidates with 3 or more years of relevant experience may be considered for positions involving greater ownership of forecasting systems, experimental strategy, stakeholder engagement, and analytical project delivery.

Relevant experience may include:

• Developing and evaluating forecasting models for business or operational metrics.
• Conducting A/B tests and interpreting experimental results.
• Performing exploratory data analysis and statistical investigations.
• Building predictive models and validating their performance.
• Using Python and SQL to analyze complex datasets.
• Preparing analytical dashboards, reports, and business recommendations.
• Collaborating with technical and business teams to deliver data-driven solutions.

Candidates should be prepared to discuss relevant projects, analytical decisions, challenges encountered, and the measurable outcomes of their work. Professional experience is beneficial, but practical skills and demonstrated problem-solving ability may also be considered.

5. Working Hours

Employment Type: Full-time, subject to the specific vacancy.

Working Arrangement: Remote, hybrid, or office-based, depending on the position and business requirements.

Typical Schedule: Approximately 40 hours per week for a full-time position, subject to the applicable employment agreement and local regulations.

Work Schedule: Standard business hours or an agreed schedule aligned with team and stakeholder requirements.

Flexibility: Flexible working arrangements may be available depending on project needs, team coordination, and organizational policies.

Meetings and Collaboration: Employees may be required to attend project meetings, planning sessions, technical reviews, and stakeholder presentations.

Additional Hours: Occasional schedule adjustments may be required during important project milestones, subject to applicable policies and regulations.

Final working hours, employment conditions, time-zone expectations, and work location will be communicated during the recruitment process.

6. Knowledge, Skills, and Abilities

Knowledge

Strong understanding of descriptive and inferential statistics.

Knowledge of time-series forecasting, trend analysis, seasonality, and predictive modeling.

Understanding of experimental design, hypothesis testing, and causal inference fundamentals.

Familiarity with machine learning workflows and model evaluation techniques.

Awareness of data quality, privacy, security, and responsible data usage.

Understanding of business performance indicators and the relationship between analytical findings and business objectives.

Skills

Ability to develop, test, and improve statistical and machine learning models.

Proficiency in manipulating datasets and automating analytical tasks with Python and SQL.

Ability to design experiments and interpret results using appropriate statistical methods.

Strong data visualization and analytical reporting capabilities.

Ability to document code, assumptions, methodologies, and results clearly.

Effective time management, prioritization, and project coordination.

Ability to communicate technical findings to different audiences.

Abilities

Ability to work independently while collaborating effectively with a multidisciplinary team.

Ability to manage ambiguity and develop structured approaches to unfamiliar problems.

Ability to identify limitations in datasets and analytical methods before drawing conclusions.

Ability to balance statistical rigor with practical business requirements.

Ability to adapt to changing priorities, emerging technologies, and evolving analytical needs.

Ability to maintain confidentiality and handle sensitive business information responsibly.

Ability to learn new tools, techniques, and domain-specific concepts quickly.

Ability to make recommendations supported by evidence, measurable objectives, and transparent assumptions.

7. Benefits

The Elite Job may offer a competitive benefits package depending on the position, employment type, work location, and applicable company policies.

Potential benefits may include:

• Competitive Compensation: Salary aligned with the candidates skills, experience, qualifications, and role responsibilities.
• Flexible Working Options: Remote or hybrid arrangements where available and appropriate for the position.
• Professional Development: Opportunities to strengthen skills in forecasting, experimentation, machine learning, and advanced analytics.
• Learning Opportunities: Access to relevant courses, technical resources, workshops, and knowledge-sharing activities where provided.
• Career Growth: Opportunities to progress into senior data science, machine learning, analytics leadership, or specialized forecasting roles, subject to performance and available vacancies.
• Collaborative Environment: Opportunities to work with professionals across data engineering, product development, business operations, and analytics.
• Meaningful Projects: Opportunities to solve practical problems and contribute to evidence-based decision-making.
• Performance Recognition: Recognition for quality work, analytical contributions, and successful project delivery in accordance with company policies.
• Work-Life Balance: Support for sustainable working practices and effective workload management where available.
• Employment Benefits: Any applicable leave, insurance, retirement, or other statutory and company benefits will depend on the employment arrangement and local requirements.

Specific benefits, eligibility conditions, and compensation details should be confirmed with the recruitment team before accepting an offer.

8. Why Join The Elite Job?

Joining The Elite Job as a Data Scientist – Forecasting and Experiment Analysis offers an opportunity to apply analytical expertise to practical business challenges while developing a strong foundation in predictive modeling and evidence-based decision-making.

Make an Impact Through Data: Use forecasting and experimentation to help teams understand trends, assess business initiatives, and make better-informed decisions.

Develop Advanced Analytical Skills: Strengthen expertise in time-series forecasting, statistical inference, machine learning, experiment design, and model evaluation.

Collaborate Across Disciplines: Work with professionals who bring different perspectives to data-driven problem-solving and business improvement.

Build a Strong Professional Portfolio: Develop experience in analytical projects, reproducible workflows, statistical reporting, and forecasting solutions that demonstrate practical capabilities.

Grow Your Career: Build a pathway toward senior data scientist, forecasting specialist, experimentation scientist, machine learning engineer, or analytics leadership roles, depending on your interests and opportunities.

Encourage Continuous Learning: Expand your knowledge of emerging analytical techniques and tools while improving your ability to solve complex problems.

Connect Technical Work With Business Outcomes: Go beyond building models by helping stakeholders understand what the results mean, where uncertainty exists, and how insights can inform practical decisions.

The Elite Job welcomes candidates who combine technical curiosity with analytical discipline, clear communication, and a commitment to producing reliable, responsible, and useful insights.