Deloitte

location-iconDeloitte

Sr. Data Scientist

location-iconReading, MA, 01867

jobtype-iconPart Time, Full Time

estimated-salary-icon$119,871 per year

dateposted-iconPosted 9 days ago

Apply Now

location-iconActively Hiring

About the Role

Do you want to build your brand by working for a leading consulting firm that drives eminence in the marketplace? Are you interested in leveraging your analytical skills and strategic ideas to improve mission execution? If so, Deloitte could be the place for you!

Our Government and Public Services (GPS) Strategy and Analytics team brings deep industry expertise, rigorous analytical capabilities, and a pragmatic mindset to solve our client's most complex business challenges. Join us to design our clients' roadmap to the future and transform the public sector marketplace.

Work You'll Do

As a Senior Data Scientist, you will work directly with federal clients to define strategies, drive technical development, and create cutting-edge AI tools and services. Your work will encompass advanced methodologies, including:

  • AI & Machine Learning Development: Utilize GANs, CNNs, RNNs, LSTMs, BERT, and other models to solve complex problems.
  • Thought Leadership: Produce whitepapers, presentations, reports, and executive briefings to support AI solutions.
  • Client Strategy: Guide federal clients on AI strategy and development, perform exploratory data analysis, build and validate models, and deploy models in both on-premise and cloud environments.
  • Technical Leadership: Lead efforts in scalable data systems, repeatable processes, and best practices for new AI methodologies.
  • Collaboration: Work cross-functionally with scientists, data engineers, federal account managers, and leadership to ensure project delivery success.

About the Team

SFL Scientific, a Deloitte business, is a U.S.-based data science consulting firm specializing in machine learning and AI technologies. The team works on complex, high-impact AI systems leveraging data streams from advanced aerospace, defense, intelligence, and energy systems within the Government & Public Services (GPS) sector.

Qualifications

Required:

  • Master’s or Ph.D. in a STEM field preferred, with expertise in Artificial Intelligence and engineering technologies.
  • 5+ years of experience in AI/ML algorithm development and data analysis (e.g., NLP, time-series analysis, computer vision).
  • 3+ years of experience in programming and data science tools (Python, Keras, TensorFlow, PyTorch, Pandas, Scikit-learn, Jupyter, etc.).
  • 3+ years of experience in deployment and optimization (e.g., Kubernetes, Docker, NVIDIA TensorRT/Triton, RAPIDs, Kubeflow, MLflow).
  • Ability to obtain and maintain a government security clearance.
  • Must be legally authorized to work in the U.S. without employer sponsorship.
  • Willingness to travel up to 5%.

Preferred:

  • 3+ years of experience in cloud deployment (AWS, Azure, GCP), including scaling solutions in AWS SageMaker.
  • Strong problem-solving and troubleshooting skills.
  • Experience coaching or mentoring junior staff.

Additional Information

For applicants requiring accommodations, please visit: Deloitte Assistance for Disabled Applicants.

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FAQ's

Find the answers for the most frequently asked questions below

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A data scientist is a professional who uses statistical methods, machine learning algorithms, and data analysis techniques to extract insights, trends, and patterns from complex data sets, often to inform business decisions and strategy.

Yes, a data scientist can work from home. They primarily work with data, which can be accessed remotely, and their tasks often involve analysis, modeling, and interpretation, which can be done independently. However, the specific work arrangements may vary depending on the company's policies and project requirements.

Data scientists analyze and interpret complex data to help organizations make informed decisions. They use statistical models, machine learning algorithms, and data visualization tools to extract insights from large datasets. They also often work on predictive modeling and data-driven problem-solving.

Yes, R is a useful tool for data science, particularly for statistical analysis, data visualization, and machine learning tasks. However, Python is also widely used in the field and offers additional libraries for data manipulation and deep learning. The choice between R and Python often depends on the specific needs of the project and the individual's familiarity with each language.

A data scientist analyzes and interprets complex data to help organizations make informed decisions. They use statistical models, machine learning algorithms, and data visualization tools to extract insights and make predictions. They also communicate their findings to stakeholders in a clear and actionable manner.

Yes, data scientists are scientists. They apply scientific methods, mathematics, and statistics to extract insights and knowledge from data. They work in various fields, such as technology, finance, healthcare, and more, to analyze and interpret complex data sets.

A data scientist analyzes and interprets complex data to help organizations make informed decisions. They use statistical models, machine learning algorithms, and data visualization tools to extract insights and make predictions. They also communicate their findings to stakeholders in a clear and actionable manner.

Data scientists, like many professionals, can earn a significant income, but their salary can vary greatly depending on factors such as location, industry, and level of experience. However, it's important to note that wealth is not solely determined by income and can also depend on factors such as spending habits, investments, and career longevity.

Yes, data scientists remain in high demand due to the increasing importance of data analysis and machine learning in various industries. The role of a data scientist is crucial for businesses seeking to leverage data for decision-making and innovation.

A good data scientist should possess strong analytical skills, proficiency in programming languages like Python or R, and a deep understanding of statistical methods and machine learning algorithms. They should also be able to communicate complex data insights effectively to non-technical stakeholders.