Data Scientist

Why CiviTronix?

At CiviTronix, we believe that data-driven decision-making is at the heart of innovation in the engineering industry. As a Data Scientist, you will have the opportunity to make a direct impact on our projects, processes, and client outcomes by leveraging the power of data. You'll be working in a dynamic, collaborative environment that encourages continuous learning and professional development.

Note: Strictly for candidates with the Unites States only.

The Data Scientist will be responsible for analyzing large sets of structured and unstructured data to uncover actionable insights, support decision-making, and solve complex problems across a variety of engineering and infrastructure domains. You will leverage advanced statistical, mathematical, and machine learning models to derive insights that enhance project outcomes, improve efficiencies, and contribute to the long-term success of CiviTronixs operations.

As a Data Scientist at CiviTronix, you will work closely with engineering, project management, and business teams to identify opportunities for data-driven improvements. This role offers an exciting opportunity to be at the forefront of applying data science to optimize engineering solutions, predict system behaviors, and support strategic business decisions.

Key Responsibilities:

1. Data Collection & Preparation:
   • Collect, clean, and preprocess data from various internal and external sources, including engineering reports, sensors, geospatial data, environmental data, and more.
   • Develop and maintain data pipelines that ensure efficient data storage, retrieval, and processing for analysis.
   • Collaborate with teams across the organization to identify relevant data sources and ensure data quality and integrity.
2. Advanced Data Analysis & Modeling:
   • Apply advanced statistical methods and machine learning algorithms to analyze complex datasets and generate insights that drive business value.
   • Develop predictive models and algorithms for engineering applications such as project forecasting, resource allocation, risk management, and performance optimization.
   • Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies within datasets, providing actionable insights to stakeholders.
3. Collaboration with Engineering Teams:
   • Work closely with engineering teams to understand their data needs, translate business problems into data science solutions, and identify opportunities for data-driven process improvements.
   • Support engineering teams by providing data-driven recommendations and insights for decision-making, performance optimization, and risk mitigation.
4. Data Visualization & Reporting:
   • Create compelling visualizations and dashboards to communicate complex data insights to both technical and non-technical stakeholders.
   • Use tools like Tableau, Power BI, or custom-built solutions to present results in a clear and actionable manner.
   • Prepare reports that summarize findings, recommendations, and impact, and present them to senior leadership, project managers, and clients.
5. Machine Learning & Automation:
   • Design, build, and deploy machine learning models that automate decision-making processes or improve operational efficiencies across projects and teams.
   • Evaluate and fine-tune models over time to ensure they provide the best possible predictions and insights.
   • Work with software engineers and developers to deploy models into production environments and ensure their scalability and performance.
6. Predictive & Prescriptive Analytics:
   • Build predictive models to forecast key metrics and outcomes, such as project costs, timelines, resource needs, and environmental impact.
   • Develop prescriptive analytics tools to recommend optimal courses of action based on data-driven insights, improving project delivery and client satisfaction.
7. Data Governance & Compliance:
   • Ensure that all data science work is conducted in compliance with company policies, industry regulations, and data privacy laws.
   • Collaborate with the IT and compliance teams to ensure that data is stored securely and adheres to governance standards.
8. Continuous Learning & Innovation:
   • Stay up to date with the latest developments in data science, machine learning, and artificial intelligence to apply cutting-edge techniques to solve business challenges.
   • Continuously evaluate new tools, technologies, and methodologies to improve data analysis capabilities and efficiencies.
9. Cross-Department Collaboration & Support:
   • Collaborate with various teams, including project management, marketing, and finance, to provide insights and support strategic initiatives.
   • Work with business analysts to translate business requirements into data-driven solutions and reports.

Required Qualifications:

• Education:
  • Masters or Ph.D. in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
• Experience:
  • 3+ years of experience in data science, data analysis, or a similar analytical role, ideally within engineering, infrastructure, or technical services.
  • Hands-on experience with machine learning algorithms, statistical modeling, and data analysis techniques.
  • Experience working with large datasets and implementing data processing pipelines.
• Technical Skills:
  • Strong proficiency in programming languages such as Python, R, or Julia for data analysis and machine learning.
  • Expertise in data manipulation and analysis libraries (e.g., Pandas, NumPy, SciPy, TensorFlow, Keras).
  • Solid experience with machine learning frameworks and tools (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
  • Experience with big data tools and platforms (e.g., Hadoop, Spark, AWS, Azure).
  • Knowledge of database technologies (SQL, NoSQL) and data warehousing concepts.
  • Proficiency in data visualization tools like Tableau, Power BI, or similar platforms.
  • Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) and related services for data science applications.
• Analytical & Problem-Solving Skills:
  • Strong ability to analyze complex data, recognize patterns, and draw actionable conclusions.
  • Excellent problem-solving skills, with the ability to break down complex business problems into manageable data science tasks.
• Communication & Collaboration:
  • Excellent communication skills, with the ability to explain complex data-driven insights to both technical and non-technical stakeholders.
  • Strong teamwork abilities and experience collaborating with cross-functional teams (engineering, product, management).
  • Ability to present data insights clearly through reports, presentations, and visualizations.

Preferred Qualifications:

• Experience working in the engineering, infrastructure, or environmental sectors is a plus.
• Familiarity with geospatial data analysis and tools (e.g., GIS software, spatial analysis techniques) is a plus.
• Knowledge of optimization techniques for large-scale operations or resource management is a bonus.
• Experience with automated reporting and business intelligence tools is preferred.

Pay rate: $55.00 - $72.00 / hour

Location: Remote (United States Only)

Benefits

• 401(k)
• 401(k) matching
• Health insurance
• Dental insurance
• Life insurance
• Paid time off

Schedule:

• 8 hour shift
• Monday to Friday