7,199 Senior Data Scientist jobs in the United States

We are seeking a highly analytical and innovative Data Scientist to transform raw data into actionable insights that guide strategic decisions. The ideal candidate will leverage expertise in statistics, programming, and machine learning to solve complex business challenges and drive data-informed growth.

Key Responsibilities:

• Collect, clean, and prepare large structured and unstructured datasets

• Apply statistical analysis and machine learning techniques to build predictive and prescriptive models

• Develop algorithms and data pipelines to optimize business processes

• Create dashboards, reports, and visualizations that clearly communicate findings to stakeholders

• Collaborate with product managers, engineers, and business teams to identify opportunities for data-driven improvements

• Monitor, validate, and retrain models to ensure accuracy and reliability over time

• Document processes, models, and methodologies for knowledge sharing and reproducibility

• Stay up-to-date with the latest tools, techniques, and trends in data science and AI

Qualifications:

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or related field (PhD is a plus)

• Proven experience as a Data Scientist or in a related analytical role

• Strong proficiency in Python, R, or SQL

• Experience with machine learning libraries (scikit-learn, TensorFlow, PyTorch)

• Solid understanding of statistical methods and data modeling

• Familiarity with big data tools (Spark, Hadoop) and cloud platforms (AWS, Azure, GCP)

• Excellent problem-solving and critical-thinking skills

• Strong communication skills to explain technical results to non-technical stakeholders

Employment Type: Full-time / Hybrid / Remote

Salary Range: $95,000 – $50,000 annually (≈ $4 – $7 per hour), depending on experience, location, and industry

Why Join Us?

• Opportunity to work on impactful projects that shape business strategy

• Collaborative environment with talented cross-functional teams

• Access to cutting-edge data science and AI technologies

• Competitive compensation, benefits, and career development opportunities

Hitachi Energy is seeking a talented Data Scientist to join our Data Analytics team. The ideal candidate will be responsible for analyzing complex datasets, developing machine learning models, and providing actionable insights to drive business decisions.

• Analyze large datasets to identify trends and patterns
• Develop predictive models using machine learning algorithms
• Collaborate with cross-functional teams to solve business problems
• Communicate findings to stakeholders through reports and presentations
• Stay current on industry trends and best practices in data science

• Bachelor's degree in Computer Science, Statistics, or related field
• Proven experience in data analysis and machine learning
• Proficiency in programming languages such as Python or R
• Strong problem-solving skills and attention to detail
• Excellent communication and teamwork abilities

If you are passionate about data science and eager to make an impact in a dynamic environment, we want to hear from you!

If you are passionate about data science and eager to make an impact in a dynamic environment, we want to hear from you!

1. Data Collection and Acquisition:
   • Data Sourcing: Gather structured and unstructured data from various internal and external sources (e.g., databases, APIs, sensors, web scraping).
   • Data Integration: Combine and integrate data from different sources into a unified format for analysis.
2. Data Cleaning and Preprocessing:
   • Data Cleaning: Handle missing, inconsistent, or incorrect data using data-cleaning techniques such as imputation, outlier detection, and normalization.
   • Data Transformation: Prepare data by transforming it into the proper format or structure required for analysis (e.g., data normalization, encoding categorical variables).
   • Feature Engineering: Select and create relevant features (variables) from raw data to improve model performance.
3. Exploratory Data Analysis (EDA):
   • Statistical Analysis: Perform statistical analysis to identify trends, patterns, and relationships within the data.
   • Visualization: Create visualizations (e.g., histograms, scatter plots, box plots) to identify patterns or anomalies in the data.
   • Hypothesis Testing: Conduct hypothesis testing to validate assumptions or test theories about the data.
4. Model Building and Development:
   • Model Selection: Choose the appropriate machine learning algorithms (e.g., regression, classification, clustering) based on the problem.
   • Algorithm Training: Train and fine-tune models using techniques like cross-validation, hyperparameter tuning, and regularization.
   • Evaluation: Evaluate model performance using metrics such as accuracy, precision, recall, F1 score, and AUC-ROC curves, depending on the problem type.
5. Deployment and Implementation:
   • Model Deployment: Deploy machine learning models into production environments where they can generate insights and inform decisions in real-time.
   • Automation: Automate model training and data pipelines for continuous improvement and updates.
   • Collaboration: Work with software engineers to integrate models into applications or business processes.
6. Reporting and Communication:
   • Data Insights: Present findings to non-technical stakeholders in an understandable way, including actionable recommendations.
   • Data Storytelling: Create compelling narratives around data insights to influence business strategies or decisions.
   • Documentation: Document processes, models, and results to ensure reproducibility and maintainability.
7. Continuous Learning and Improvement:
   • Research and Development: Stay up-to-date with the latest trends, tools, and techniques in data science and machine learning.
   • Experimentation: Continuously experiment with new algorithms, models, and features to improve performance.

1. Programming and Software Tools:
   • Python & R: Proficiency in Python and/or R for data analysis, statistical modeling, and machine learning.
   • Libraries/Frameworks: Knowledge of machine learning libraries such as Scikit-learn , TensorFlow , Keras , PyTorch , XGBoost , and Keras .
   • Data Manipulation Tools: Experience with Pandas , NumPy , and Dask for data cleaning, manipulation, and analysis.
   • Big Data Tools: Familiarity with big data processing tools like Apache Spark , Hadoop , and HDFS .
   • Database Technologies: Experience with SQL for querying relational databases and familiarity with NoSQL databases (e.g., MongoDB , Cassandra ).
   • Version Control: Proficiency with version control systems like Git to manage code and collaborate on projects.
2. Machine Learning & Statistical Knowledge:
   • Supervised & Unsupervised Learning: Strong understanding of machine learning algorithms, including linear/logistic regression, decision trees, random forests, SVMs, K-means, and clustering algorithms.
   • Deep Learning: Familiarity with neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
   • Statistical Analysis: Knowledge of statistical methods such as hypothesis testing, p-values, confidence intervals, ANOVA, and Bayesian methods.
   • Model Evaluation: Ability to evaluate model performance using various metrics (e.g., RMSE, confusion matrix, ROC curve, cross-validation).
   • Optimization Techniques: Familiarity with techniques like gradient descent , hyperparameter tuning , and ensemble methods .
3. Data Visualization and Reporting:
   • Data Visualization Tools: Proficiency in tools like Matplotlib , Seaborn , ggplot2 , and Tableau to create meaningful visualizations.
   • Business Intelligence Tools: Familiarity with BI tools such as Power BI , Looker , or Qlik for generating reports and dashboards.
4. Problem-Solving and Critical Thinking:
   • Ability to approach complex problems analytically, using data to identify patterns, make predictions, and develop solutions.
   • Strong mathematical and analytical thinking skills for breaking down problems and understanding the underlying data structures.
5. Collaboration and Communication:
   • Ability to work collaboratively in multidisciplinary teams (with software engineers, business analysts, etc.).
   • Strong written and verbal communication skills to explain complex findings to non-technical stakeholders.
   • Experience presenting data insights in a clear, concise manner to influence business decisions.

1. Entry-Level (0-2 years):
   • Experience: Typically 0–2 years of experience in data analysis, data engineering, or entry-level data science roles (e.g., Data Analyst, Junior Data Scientist).
   • Skills Development: Experience with programming, statistical analysis, data wrangling, and machine learning algorithms through internships, projects, or coursework.
2. Mid-Level (2-5 years):
   • Experience: 2–5 years of hands-on experience working with real-world datasets, deploying machine learning models, and collaborating with cross-functional teams.
   • Project Involvement: Involvement in significant data science projects, including end-to-end processes from data collection and cleaning to model deployment.
   • Specialization: Potential specialization in areas like Natural Language Processing (NLP) , computer vision , predictive modeling , or reinforcement learning .
3. Senior-Level (5+ years):
   • Experience: 5+ years of experience in data science or related fields, with a proven track record of delivering impactful projects and scaling solutions.
   • Leadership/Management: Often involves mentoring junior data scientists, leading data science teams, or driving data science strategy for an organization.
   • Complex Problem Solving: Experience tackling complex business problems and leading high-stakes data science initiatives.

1. Bachelor’s Degree:
   • A bachelor’s degree in a relevant field, such as Computer Science , Mathematics , Statistics , Engineering , Physics , or Economics .
   • Courses in data analysis , machine learning , algorithms , statistics , linear algebra , and programming are typically required.
2. Master’s or Ph.D. (Preferred but Not Always Required):
   • A Master’s degree in Data Science , Artificial Intelligence , Machine Learning , Statistics , or a related field is often preferred, especially for specialized or senior roles.
   • A Ph.D. is typically required for highly specialized roles, particularly in research or academic settings (e.g., Natural Language Processing (NLP) or Computer Vision ).
3. Certifications (Optional but Beneficial):
   • Data Science Certifications: Courses or certifications from platforms like Coursera , edX , or Udacity can enhance your skills, such as the Data Science Professional Certificate or Machine Learning by Stanford University .

   • Machine Learning Certifications: Google AI or Microsoft’s Data Science Certification are great ways to validate your skills.

We are seeking a passionate and skilled Data Scientist to join our innovative team, where you'll have the opportunity to utilize your expertise in data analysis and machine learning to drive impactful business decisions and outcomes. In this role, you will be responsible for collecting, analyzing, and interpreting complex data sets to uncover trends, patterns, and insights that will inform strategic initiatives across the organization. As a Data Scientist, you will collaborate closely with cross-functional teams, including product management, engineering, and marketing, to develop data-driven solutions that enhance customer experiences and optimize operational efficiency. Your findings will not only influence product development but also play a crucial role in shaping the overall business strategy. The ideal candidate will possess strong analytical skills, a deep understanding of statistical methodologies, and experience with data visualization techniques. You will also have the opportunity to work with cutting-edge tools and technologies, contributing to a culture of continuous learning and innovation. If you are a self-motivated individual with a passion for data and an eagerness to solve complex problems, we encourage you to apply and become an integral part of our dynamic team.

• Collect, clean, and analyze large datasets to extract meaningful insights.
• Develop predictive models and machine learning algorithms to support business objectives.
• Collaborate with cross-functional teams to identify and prioritize data-driven projects.
• Visualize data findings using appropriate tools to communicate results effectively.
• Monitor and assess the performance of algorithms and models, making adjustments as necessary.
• Conduct experiments and A/B testing to validate hypotheses and inform decision-making.
• Stay updated with the latest industry trends, technologies, and methodologies in data science.

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, or a related field.
• Proven experience as a Data Scientist or in a similar analytical role.
• Strong proficiency in programming languages such as Python or R.
• Familiarity with data visualization tools (e.g., Tableau, Power BI, or similar).
• Experience with machine learning frameworks (e.g., TensorFlow, Scikit-learn).
• Solid understanding of statistical analysis and methodologies.
• Excellent communication skills to present findings to technical and non-technical stakeholders.

Job Family :

Travel Required :

Clearance Required :

What You Will Do :

This Data Scientist role will work as part of a Data & AI consulting team to support data visualization, business analytics, data management, and digital engineering processes for major Program Executive Offices (PEOs) at the National Geospatial-Intelligence Agency (NGA). A strong understanding of data visualization is essential to help the PEOs understand how to effectively monitor their operations through reporting dashboards, tools, and the creation of data sets. This role will be located on client site and requires excellent communication skills to coordinate status updates and visualization or digital engineering initiatives. Seeking a candidate with the ability to proactively identify program needs and help the office mature its operational reporting, business analytics, and data processes in line with technology/data solutions advancement. Specific duties will include:

• Integrate, develop, and maintain analytic visualizations and tools to evaluate and communicate office status/operations.
• Work with multiple types of data sources, such as Jira, Excel, and SQL databases to develop visualizations; work with data at varying maturity levels and create relationships between disparate sources to maximize analyses.
• Interpret a wide range of data for the purpose of measuring a major technology program’s performance and impact to mission. Help communicate that performance measurement to Senior leaders and inform decisions such as investment/divestment, prioritization, and operational strategies.
• Use storytelling and user interface design methods to develop/maintain Tableau dashboards to address program management needs, catered to specific audience groups. Design dashboards to be visually appealing, intuitive, and user friendly, containing a high volume of information in concise graphics/tables/metrics.
• Manage an inventory of implemented dashboards, other analytic products and current product backlog for implementation.
• Demoing visualizations/analytic products to Agency Senior and program leadership – utilize effective communication skills to convey purpose, use cases applicability, and impact, and solicit feedback for product enhancement.
• Understanding of effective data management and data visualization project documentation such as SOPs, data definitions/dictionaries, data process flows, Confluence pages
• Help evolve program analytics with automation, connecting to unstructured sources, and other Enterprise solutions (such as data format/process to support LLM)

What You Will Need :

• An ACTIVE and MAINTAINED TS/SCI Federal or DoD security clearance; must UPGRADE and MAINTAIN a TS/SCI with a COUNTERINTELLIGENCE (CI) polygraph
• Bachelor's Degree
• Minimum of EIGHT (8) years of working experience
• Demonstrated experience developing visualizations and conducting data analysis
• Demonstrated experience utilizing computer programs, software, or some coding language

What Would Be Nice To Have :

• Demonstrated experience developing visualizations in Tableau
• Demonstrated ability to proactively identify methods and approaches to expand and enhance the analytic capacity.
• Demonstrated experience working with commercial-off-the-shelf (COTS) statistical software or tools for data visualization (i.e., SPSS, SAS, MatLab, etc.).
• Demonstrated experience data mining, to include developing, manipulating, or maintaining databases.
• Experience with Python, MySQL, D3, SPSS, SAS, Visual Basic, or R to summarize statistical data and create documents, reports and presentations.
• Demonstrated experience effectively communicating with various partners, stakeholders, or customers on the value of statistical and data science methods.
• Demonstrated experience embracing and participating in a culture of knowledge sharing to broaden the understanding of advanced methods.
• Understanding and/or experience developing AI/ML or working with AI platform tools

What We Offer :

Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace.

Benefits include:

• Medical, Rx, Dental & Vision Insurance

• Personal and Family Sick Time & Company Paid Holidays

• Position may be eligible for a discretionary variable incentive bonus

• Parental Leave and Adoption Assistance

• 401(k) Retirement Plan

• Basic Life & Supplemental Life

• Health Savings Account, Dental/Vision & Dependent Care Flexible Spending Accounts

• Short-Term & Long-Term Disability

• Student Loan PayDown

• Tuition Reimbursement, Personal Development & Learning Opportunities

• Skills Development & Certifications

• Employee Referral Program

• Corporate Sponsored Events & Community Outreach

• Emergency Back-Up Childcare Program

• Mobility Stipend

About Guidehouse

Guidehouse is an Equal Opportunity Employer–Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation.

Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco.

If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation.

All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains including @guidehouse.com or .  Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse.  Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process.

If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse’s Ethics Hotline. If you want to check the validity of correspondence you have received, please contact . Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant’s dealings with unauthorized third parties.

Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.