Senior Data Scientist

Descriptions 

You will be joining Azira, a global Consumer Insights platform, helping marketing and operational leaders improve their effectiveness with actionable intelligence to drive business results. Its mission is to create a more relevant world where brands are empowered to reach and build relationships with their consumers.

We are looking for a Senior Data Scientist to join our growing team, working on cutting-edge analytics and machine learning solutions. This role is not just about research—it is about delivering business value. The ideal candidate will have experience moving from research to production, ensuring that data science solutions drive measurable impact. You will be responsible for developing models, deploying them at scale, and ensuring they deliver tangible results aligned with business milestones. This role reports to the Head of Data Science

A Day in the Life

• Design and develop machine learning models (regression, classification, neural networks) that directly impact business outcomes.
• Work with large-scale datasets using big data tools such as Spark to generate actionable insights.
• Translate research into production-ready solutions, ensuring scalability, reliability, and performance.
• Deliver projects against defined business milestones, tracking progress and ensuring successful deployment.
• Optimize SQL queries to analyze structured data within cloud data storage environments.
• Implement data science solutions using Python and apply object-oriented programming (OOP) principles where necessary.
• Utilize cloud computing frameworks (preferably AWS, but experience with GCP or Azure is acceptable) to deploy and manage machine learning workflows.
• Collaborate with cross-functional teams, including engineers, business analysts, and product managers, to integrate models into production systems.
• Clearly communicate insights, technical solutions, and project progress to both technical and non-technical stakeholders.

What You Bring to the Role

• Experience: 5+ years of overall experience in data science and analytics, with a minimum of 3+ years of focused experience in a big data or data science-centric organization.
• Expert-level understanding of statistical and probabilistic methods, including experimental design, causal inference, and advanced statistical modeling. Ability to rigorously evaluate model performance, identify potential biases, and ensure statistical validity of findings.
• Deep expertise in a wide range of machine learning algorithms, including advanced regression, classification, clustering, and time-series analysis. Proven ability to select, implement, and fine-tune appropriate models for complex business problems, demonstrating a strong understanding of model trade-offs and interpretability.
• Demonstrated track record of leading the end-to-end lifecycle of complex ML projects, from ideation and research to robust production deployment, monitoring, and iteration.
• Experience delivering projects on time and aligned with business milestones.
• Advanced proficiency in SQL and data manipulation languages, with extensive experience working with large-scale, complex datasets across various cloud data platforms. Ability to optimize SQL queries for performance and design efficient data pipelines.
• Experience architecting, deploying, and managing data science solutions on cloud platforms, preferably AWS (GCP or Azure also acceptable).
• Proficiency in Python and strong understanding of software engineering best practices, including advanced OOP concepts, data structures, algorithms, and version control.
• Significant experience in designing and implementing scalable data processing pipelines using big data technologies like Spark.
• A data science professional aligns data initiatives with business goals, communicates insights effectively, solves problems creatively, and collaborates with cross-functional teams.

Nice-to-Have Qualifications:

• Experience with MLOps frameworks, such as MLflow, for managing machine learning lifecycles.
• Proficiency in R programming language for statistical analysis.
• Understanding of CI/CD pipelines for machine learning deployment.
• Domain knowledge of geospatial datasets and mobility data.
• Experience with time series analysis for forecasting and trend detection.
• Basic understanding of LLMs (Large Language Models) and Generative AI.