About the Role
SafetyCulture is a customer and product-led SAAS company, with a mission to help working teams get better every day. Our technology platform and products give frontline workers a voice and leaders the visibility to make smart decisions when driving safety, quality and continuous improvement. We are seeking a Data Scientist who will empower the organisation to make data-driven decisions by transforming raw data into meaningful stories, aligning with SafetyCulture's mission to improve safety and quality standards across industries.
This newly created role exists to leverage advanced analytical techniques, including machine learning and predictive modelling, to enhance product offerings, optimise operational efficiency, and maintain SafetyCulture’s competitive edge in the market. Ultimately, this role is pivotal in shaping the company’s future through innovative data solutions that resonate with customer needs and business goals.
Key Responsibilities
- Develop innovative data models and algorithms to drive predictive insights and strategic business decisions.
- Identify trends and patterns within complex data sets to inform product development and customer experience strategies.
- Collaborate with cross-functional teams to integrate data science into all aspects of business operations and decision-making.
- Lead the implementation of machine learning and AI techniques to automate processes and enhance analytical capabilities.
- Communicate complex data findings in a clear and actionable manner to stakeholders at all levels of the organisation.
- Champion a data-driven culture, advocating for the importance of data insights in achieving organisational goals.
- Ensure the integrity and accuracy of data used for analysis, maintaining high standards of data quality and governance.
- Drive continuous improvement by staying abreast of industry trends and advancements in data science and analytics technologies.
Key Stakeholders You'll Engage With
- Product Teams: Work closely with product managers and developers to integrate data-driven features and enhancements into products.
- Marketing Department: Partner with marketing to refine customer segmentation, personalise campaigns, and measure their effectiveness.
- Sales Teams: Assist sales with lead scoring models and customer insights to optimise sales strategies and performance.
- Customer Success: Provide analytics support to customer success teams to improve customer retention and satisfaction.
- Operations: Collaborate with operations to streamline processes and identify efficiency gains through data analysis.
Required Experience & Skills
- Data Science Principles: Profound understanding of statistical analysis, predictive modelling, and machine learning algorithms.
- Programming Languages: Proficiency in languages such as Python, R, or Scala, and familiarity with SQL for database querying.
- Data Visualisation: Knowledge of data visualisation tools and techniques to present complex data insights clearly.
- Big Data Technologies: Familiarity with big data platforms like Hadoop, Spark, or Kafka.
- Domain Expertise: Awareness of the industry SafetyCulture operates in, including safety regulations and quality standards.
Key Attributes for Success
- Analytical Thinking: Ability to dissect complex data sets and extract meaningful insights.
- Problem-Solving: Strong problem-solving skills to address business challenges with data-driven solutions.
- Communication: Excellent verbal and written communication skills for articulating data findings to non-technical stakeholders.
- Collaboration: Ability to work effectively in cross-functional teams and with various stakeholders.
- Project Management: Skills in managing projects, setting timelines, and meeting deadlines.
- Curiosity: A strong desire to explore data for hidden patterns and opportunities.
- Attention to Detail: Meticulous attention to detail to ensure accuracy in data analysis and modelling.
- Adaptability: Flexibility to adapt to new tools, technologies, and changing business needs.
- Innovative Mindset: A creative approach to developing new analytical methods and solutions.
- Ethical Judgment: Commitment to ethical standards in handling and analysing data.