Data Scientist – Algorithms & Data Analytics

About the Role

We are seeking an experienced Data Scientist – Algorithms & Data Analytics to review, validate, develop, and improve the algorithms and analytical methods used across EFD technologies.

This is a hands-on role working with complex real-world sensor and operational data. You will investigate how data is processed, interpreted, correlated, and converted into meaningful diagnostic information.

You will play a key role in assessing existing algorithms, identifying opportunities for improvement, developing and testing new approaches, and establishing robust methods for measuring algorithm performance.

We are looking for someone with strong capabilities in data science, algorithm development, statistical analysis, signal processing, applied mathematics, machine learning, or engineering analytics.

Experience in electrical engineering, electrical networks, condition monitoring, sensor analytics, signal processing, RF systems, or similar industrial technologies would be advantageous but is not essential.

Key Responsibilities

Algorithm Development & Improvement

• Review and understand existing EFD data-processing and diagnostic algorithms.

Analyse current algorithm performance and identify opportunities for improvement.

• Develop new algorithms and analytical approaches for processing complex field and sensor data.
• Investigate false positives, false negatives, incorrect classifications, localisation errors, and other unexpected behaviours.
• Improve detection accuracy, reliability, sensitivity, localisation, and computational efficiency.
• Develop methods for assigning confidence levels to detections and analytical outputs.
• Establish regression testing to objectively compare algorithm changes against existing performance.
• Document algorithm assumptions, parameters, decision logic, dependencies, and limitations.

Data Analysis & Signal Processing

• Analyse large and complex datasets generated by EFD field devices.
• Identify patterns, trends, anomalies, correlations, and behaviours within sensor and time-series data.
• Characterise signals, background noise, interference, environmental effects, and other factors affecting data quality.
• Develop and evaluate analytical and signal-processing techniques including:

o Digital filtering and noise reduction

o Time-series analysis

o Frequency and spectral analysis

o Time-domain and time-frequency analysis

o Feature extraction

o Signal correlation

o Event detection and segmentation

o Classification

o Clustering

o Anomaly detection

o Statistical modelling

• Develop methods for distinguishing meaningful electrical activity from environmental noise, interference, and unrelated events.

Algorithm Validation & Performance

Develop objective methods for measuring algorithm and model performance, including:

• Detection accuracy and sensitivity
• False-positive and false-negative rates
• Processing latency
• Confidence levels
• Algorithm stability
• Performance across different network environments and asset types

Develop representative datasets and test scenarios that allow algorithm changes to be evaluated consistently.

Where possible, compare analytical outputs against real-world ground truth, including field inspections, confirmed network events, maintenance findings, laboratory testing, and other verified information.

Data Science & Machine Learning

Where appropriate, apply data science and machine-learning techniques to improve EFD analytical capabilities, including:

• Investigate opportunities to apply machine learning and advanced statistical techniques to improve EFD’s analytical capabilities.
• Develop features and models for detection, classification, localisation, anomaly identification, and predictive analysis.
• Investigate explainable and interpretable approaches to ensure analytical outputs can be understood by engineering and operational users.
• Ensure advanced analytical approaches deliver measurable operational value rather than complexity alone.

Research & Development

• Design structured experiments to test new ideas, algorithms, models, and analytical approaches.
• Develop prototypes and proof-of-concept solutions using real-world datasets.
• Research emerging methods in data science, signal processing, machine learning, statistical analysis, sensor analytics, and electrical condition monitoring.
• Evaluate new techniques against existing approaches using measurable performance criteria.
• Support field trials and controlled testing where additional data is required to validate hypotheses.
• Translate successful research outcomes into practical production-ready solutions.
• Identify opportunities for new intellectual property and differentiated EFD capabilities.

Production & Software Integration

• Work closely with software engineers to transition validated algorithms and models from research prototypes into production.
• Develop clean, maintainable, and testable analytical code.
• Ensure algorithms are scalable and suitable for operational deployment.
• Support automated testing and ongoing algorithm-performance monitoring.
• Consider processing latency, computational requirements, data volumes, and deployment constraints when designing solutions.
• Support investigation and resolution of algorithm-related issues identified in production.
• Maintain appropriate versioning, documentation, and traceability of algorithm and model changes.

Essential Skills & Experience

We are looking for candidates with experience in several of the following areas:

• Data science, algorithm development, applied research, or advanced analytics.
• Strong analytical and problem-solving skills.
• Experience working with complex, noisy, incomplete, or real-world datasets.
• Strong understanding of statistical analysis and data modelling.
• Experience developing, reviewing, testing, and improving algorithms.
• Experience with time-series or sensor data.
• Experience with machine learning, pattern recognition, anomaly detection, or classification.
• Experience designing experiments and objectively evaluating algorithm performance.
• Proficiency in Python and associated data science and scientific computing tools.
• Experience with libraries and frameworks such as NumPy, Pandas, SciPy, scikit-learn, or equivalent.
• Experience with data visualisation and exploratory data analysis.
• Understanding of software-development practices including source control, code review, testing, and documentation.
• Ability to translate research concepts and prototypes into practical solutions.
• Strong technical communication and documentation skills.

Qualifications

A bachelor’s, master’s, or doctoral degree in a relevant discipline such as:

• Data Science
• Computer Science
• Electrical or Electronic Engineering
• Applied Mathematics
• Statistics
• Physics
• Signal Processing
• Machine Learning / Artificial Intelligence
• Telecommunications Engineering
• Or another relevant quantitative or engineering discipline

Equivalent industry and applied R&D experience will also be considered.

Advanced academic qualifications are valued where they demonstrate strong applied research and problem-solving capabilities.

Pay: $110,000.00 – $120,000.00 per year

Benefits:

• Gym membership
• Work from home

Ability to commute/relocate:

• Melbourne VIC: Reliably commute or planning to relocate before starting work (Preferred)

Application Question(s):

• What is your current status in Australia? if not Australlan citizen or oermanent resident, what is your current visa and when it will expire?
• What is your salary expectation?

Education:

• Bachelor Degree (Required)

Experience:

• Data Scientist – Algorithms & Data Analytics: 5 years (Required)

Work Authorisation:

• Australia (Required)

Work Location: In person