Data Scientist

Company Overview

Throughout our worldwide network of experts, clients and communities, we are renowned for our leadership in fire protection engineering – a legacy of responsibility we have proudly upheld since 1939. Today, our expertise extends broadly across closely related security and risk-based fields – from accessibility consulting and risk analysis to process safety, forensic investigations, security risk consulting, emergency management, digital innovation and more.

Our engineers and consultants collaborate to solve complex safety and security challenges, ensuring our clients can protect what matters most. For over 80 years, we have helped mitigate risks that threaten lives, property and reputations. Through technology, expertise and industry-leading research, we remain dedicated to our purpose of making our world safe, secure and resilient.

At Jensen Hughes, we believe that creating and sustaining a culture of trust, integrity and professional growth starts with putting our people first. Our employees are our greatest strength, and we value the unique perspectives and talents they bring to our organization.

Our wide range of Global Employee Networks connect people from across the organization, supporting career development and providing forums for individuals to share experiences on topics they're passionate about. Together, we are cultivating a connected culture where everyone has the opportunity to learn, grow and succeed together.

Job Overview

We are seeking a skilled and motivated Data Scientist to join our team. The ideal candidate will leverage data science techniques to develop predictive models, generate insights, and support strategic decision-making, particularly in revenue forecasting and financial analytics. This role offers the chance to work in a fast-paced environment and advance your career within a supportive and diverse team.

This is a remote role in the U.S. and will be responsible for supporting global operations.

Responsibilities:

• Apply statistical techniques (regression, distribution analysis, hypothesis testing) to
  derive insights from data and create advanced algorithms and statistical models
  such simulation, scenario analysis, and clustering
• Explain complex models (e.g., RandomForest, XGBoost, Prophet, SARIMA) in an
  accessible way to stakeholders
• Visualize and present data using tools such as Power BI, ggplot, and matplotlib
• Explore internal datasets to extract meaningful business insights and communicate
  results effectively and write efficient, reusable code for data improvement,
  manipulation, and analysis
• Manage project codebase using Git or equivalent version control systems
• Design scalable dashboards and analytical tools for central use
• Build strong collaborative relationships with stakeholders across departments to
  drive data-informed decision-making while also helping in the identification of
  opportunities for leveraging data to generate business insights
• Enable quick prototype creation for analytical solutions and develop predictive
  models and machine learning algorithms to analyze large datasets and identify
  trends
• Communicate analytical findings in clear, actionable terms for non-technical
  audiences
• Mine and analyze data to improve forecasting accuracy, optimize marketing
  techniques, and informed business strategies, developing and managing tools and
  processes for monitoring model performance and data accuracy
• Work cross-functionally to implement and evaluate model outcomes

Requirements and Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics,
  Engineering, or related technical field
• 3+ years of relevant experience in data science and analytics and adept in building
  and deploying time series models
• Familiarity with project management tools such as Jira along with experience in
  cloud platforms and services such as DataBricks or AWS
• Proficiency with version control systems such as BitBucket and Python
  programming
• Experience with big data frameworks such as PySpark along strong knowledge of
  data cleaning packages (pandas, numpy)
• Proficiency in machine learning libraries (statsmodels, prophet, mlflow, scikit-learn,
  pyspark.ml)
• Knowledge of statistical and data mining techniques such as GLM/regression,
  random forests, boosting, and text mining
• Competence in SQL and relational databases along with experience using
  visualization tools such as Power BI
• Strong communication and collaboration skills, with the ability to explain complex
  concepts to non-technical audiences

#LI-KV1