ABOUT SECURITAS TECHNOLOGY:

Securitas Technology, a division of Securitas, is a global leader in integrated security solutions. With over 13,000 colleagues across 40 countries, we protect, connect, and optimize businesses of all sizes through cutting-edge technologies and unparalleled expertise. Our mission is to make the world a safer place.

REQUIRED SKILLS AND QUALIFICATIONS:

Technical Expertise: Proficiency in Google Cloud Platform (GCP), including services like BigQuery, AI Platform, and Cloud Storage. Proficiency in programming languages such as Python or R, and experience with machine learning frameworks like TensorFlow or PyTorch.
Machine Learning: Experience with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. Familiarity with natural language processing (NLP) tools like spaCy or NLTK is a plus.
Data Management: Experience with SQL and NoSQL databases, as well as cloud-based data storage solutions like Google Cloud Platform (GCP), Azure or AWS.
Data Analysis and Visualization: Expertise in data manipulation tools such as Pandas and NumPy. Experience with visualization tools like Tableau, Power BI, or Google Data Studio to create dashboards and reports.
Analytical Skills: Strong problem-solving abilities with a deep understanding of statistical analysis, predictive modelling, and data mining techniques. Curiosity to proactively identify problem and innovate their solutions for business improvement is key.

EXPERIENCE:

Significant experience in data science, with a proven track record in developing and deploying machine learning models in a business environment.

EDUCATION:

Qualification in Statistics and Machine Learning or Data Science, with 3+ Years plus experience in relevant field

SKILLS:

Communication: Excellent written and verbal communication skills, with the ability to explain complex concepts to non-technical audiences. The ability to create documentation or training materials would be beneficial.
Collaboration: Ability to work collaboratively with cross-functional teams, including IT, marketing, business leaders, engineers, and business analysts. You will collaborate and work closely with a data engineer on the team to drive your projects forward, so leadership skills are a plus.

ABOUT THE ROLE:

Are you passionate about leveraging data to drive innovation? As our Data Scientist, you will be at the forefront of transforming raw data into actionable insights that enhance our security solutions and optimize business operations. Reporting to the Senior Global Director of CX & Digital Experience, you will lead the design, development, and deployment of advanced data models, helping us to make informed decisions and achieve our strategic goals.

KEY RESPONSIBILITIES:

• Develop and implement advanced statistical models and machine learning algorithms to analyse large datasets.
• Examples of models needed: supervised learning for predictive modelling (such as churn prediction or NPS forecasting), unsupervised for clustering (such as engagement patterns and sentiment analysis), time series models to analyse trends over time (such as engagement metrics) and reinforcement learning for potentially optimising client interactions based on real-time feedback.
• Types of data available: structured (client demographics, transaction histories and engagement metrics), unstructured (such as client feedback, call recordings and interaction logs), time series data (such as interaction frequency, financial performance over time), text data (sentiment analysis from client feedback and ticket notes)
• Conduct data mining and predictive modelling to identify trends, patterns, and opportunities for business growth. Ability to demonstrate experience with the following would be beneficial;
• NLP for text data, AI and ML in customer service, call centre and client support data, customer experience management data, sales and marketing campaign data, service and invoice data, financial and accounts receivable data, CRM data, recurring revenue models, logistics and field service management data.
• Collaborate with cross-functional teams such as IT, technicians, marketing, analysts, finance and product and app development, to design and execute data-driven strategies that enhance business performance and customer experience. Some of the project work could include, but is not limited to, the following;
• Customer segmentation and personalisation, churn prediction and retention, customer journey mapping and optimisation, A/B testing and experimentation, predictive analytics for cross-sell and upsell, voice of customer analytics, automation of marketing campaigns, CLTV prediction, product recommendation systems, sentiment analysis
• Present complex data analysis results in a clear, actionable, and compelling manner to both technical and non-technical stakeholders. Leverage data storytelling techniques to translate insights into narratives that drive decision-making, and utilise interactive dashboards to visualise key metrics, making data accessible and understandable across all levels of the organisation.
• Optimise existing processes by automating routine tasks through the development of data pipelines and scalable algorithms.
• Monitor and refine data models to ensure accuracy and relevance in changing environments.
• Stay up-to-date with the latest advancements in data science and technology, applying innovative approaches to solve complex problems.