Research Engineer II (Advanced Analytics)

at  CCAM

Description:
As Research Engineer II (Advanced Analytics), you will play a pivotal role in developing and implementing cutting-edge analytics solutions to extract insights from complex datasets across various advanced manufacturing processes. Additionally, you will be responsible for generating insightful reports and technical documentation that effectively communicate findings, methodologies, and recommendations to external stakeholders and partners, enabling solution-oriented approaches and fostering collaboration.

MINIMUM QUALIFICATIONS:

• Master’s degree in Engineering, Computer Science, or a related field.
• At least 3 years of relevant experience in data analytics, machine learning, or related fields.
• Proficiency in at least one programming language commonly used in data analysis such as Python, R, or MATLAB.
• Strong understanding of basic statistical methods and data manipulation techniques.
• Demonstrated ability to work with large datasets and conduct data analysis tasks independently.

PREFERRED QUALIFICATIONS:

• Ph.D. Degree in Engineering, Computer Science, or a related field, with a focus on data analytics, machine learning, or computational science.
• This role often requires deep domain expertise in areas such as industrial systems engineering, manufacturing processes, or other specific domains.
• 5+ years of experience in developing and implementing advanced analytics solutions in industry or research settings.
• Proficiency in multiple programming languages commonly used in data science and machine learning, such as Python, R, MATLAB, or Java.
• Experience with sensor technologies, signal processing, frequency analysis, or other relevant domains.
• Strong background in machine learning algorithms, statistical modeling, and data mining techniques.
• Experience with big data technologies and frameworks such as Hadoop, Spark, or TensorFlow.
• Demonstrated ability to lead and mentor junior team members, and effectively communicate complex technical concepts to both technical and non-technical stakeholders.
• Contributions to open-source projects, publications in peer-reviewed journals or conferences, and active participation in relevant professional communities.

• Conducting research to explore innovative techniques and methodologies in the field of advanced analytics, including machine learning, statistical analysis, and predictive & simulation modeling.
• Integrating sensor data streams into analytical frameworks, leveraging sensor technologies to capture real-time data and derive actionable insights for decision-making.
• Performing frequency analysis and spectral analysis techniques to analyze time-series data, identify periodic patterns, and detect anomalies or deviations from expected behavior.
• Evaluating the performance of analytical models through rigorous testing, validation, and benchmarking against ground truth data, iterating on model designs to improve accuracy and robustness.
• Demonstrated expertise in reporting, technical writing, and documentation, including the ability to produce comprehensive reports, research papers, and technical documentation that effectively communicate findings, methodologies, and recommendations to diverse audiences.
• Designing and implementing algorithms to process and analyze large volumes of data efficiently, with a focus on accuracy, scalability, and performance optimization.
• Developing pipelines and workflows for ingesting, cleaning, and preprocessing diverse datasets from various sources, ensuring data quality and integrity throughout the process.
• Applying advanced machine learning techniques, such as supervised learning, unsupervised learning, and deep learning, to extract meaningful patterns and insights from structured and unstructured data.
• Collaborating with cross-functional teams to understand requirements, communicate findings, and drive insights-driven decision-making.