Operations Data Scientist

Quarterhill•UNAVAILABLE, UNAVAILABLE

About The Position

The Operations Data Scientist applies data science, statistical analysis, predictive modeling, and automation to improve system reliability and operational performance. This role analyzes large volumes of system, maintenance, incident, alarm, and operational data to identify trends, anomalies, and indicators of potential failures. The role will develop predictive capabilities that allow teams to identify issues earlier, improve maintenance strategies, and reduce service impacts. This position will also work closely with Technical Support, Development, and Operations to understand the applications and systems generating the data and support the implementation of analytical solutions into production environments.

Requirements

Proficiency in statistical programming languages such as Python or R.
Strong SQL skills and experience querying and analyzing relational databases.
Strong foundation in statistics, mathematics, and data analysis.
Experience with predictive modeling, statistical analysis, anomaly detection, and machine learning techniques.
Experience working with large and complex datasets.
Experience with big data technologies such as Hadoop, Spark, Hive, or similar platforms.
Familiarity with data visualization tools such as Tableau, Power BI, Matplotlib, or similar technologies.
Ability to analyze complex datasets and translate findings into actionable recommendations.
Strong analytical, problem-solving, and critical-thinking skills.
Strong technical aptitude and interest in understanding production applications and systems.
Excellent communication skills with the ability to present technical and analytical findings to technical and non-technical stakeholders.

Nice To Haves

Experience with predictive maintenance, reliability analytics, anomaly detection, or operational data.
Experience with Linux and production application environments.
Familiarity with Docker and containerized applications.
Familiarity with Git or similar source control tools.
Experience with PostgreSQL or Oracle.
Familiarity with ELK, Kafka, ActiveMQ, or similar logging and messaging technologies.
Familiarity with APIs and system integrations.
Familiarity with Java-based applications or other enterprise application technologies.
Experience supporting analytical solutions in production environments.
Experience developing automated reporting or data-processing solutions.

Responsibilities

Analyze large and complex operational datasets to identify trends, anomalies, recurring issues, and indicators of potential failures.
Develop and refine predictive models, rules, thresholds, and analytical methods.
Apply statistical analysis, anomaly detection, time-series analysis, predictive modeling, and machine learning techniques where appropriate.
Evaluate predictive results against actual failures, incidents, maintenance activities, and field results to measure effectiveness and improve accuracy.
Identify opportunities to expand predictive analytics and predictive maintenance capabilities across customer projects.
Develop Python or R-based solutions for data analysis, automation, and modeling.
Use SQL to query, combine, validate, and analyze data from relational databases.
Work with large-scale datasets and big data technologies to support analytical and predictive use cases.
Develop automated reporting, datasets, visualizations, and analytical outputs to support operational and technical decision-making.
Partner with technical teams to understand application behavior, system architecture, integrations, and data pipelines.
Use application logs, database information, system metrics, and operational data to support troubleshooting and root cause analysis.
Participate in testing and validation of analytical and technical solutions.
Identify opportunities to improve system reliability, data quality, operational efficiency, and automation.
Communicate analytical findings and recommendations clearly to technical and non-technical stakeholders.