Data Scientist & AI

We are seeking an experienced and impact-driven Data Scientist to join our this role you will be responsible for delivering end-to-end data science solutions from problem definition and data exploration to model deployment and evaluation. You will act as a technical bridge between complex data environments and business stakeholders influencing the technical direction of projectsand ensuring that our models provide quantifiable value and drive key business objectives.

• End-to-End Model Development:Design execute and deploy impactful DS projects. This includes performing Exploratory Data Analysis (EDA) feature engineering and implementing advanced modeling techniques tailored to unique data characteristics (e.g. XGBoost TensorFlow or PyTorch).
• Engineering & Reproducibility:Consistently deliver high-quality reproducible code. You will implement robust error handling conduct comprehensive model validation and establish patterns/standards for code clarity and maintainability within a Git-based workflow.
• MLOps & Deployment:Deploy models using established CI/CD pipelines and tools (e.g. MLflow Kubeflow). You will be responsible for designing scalable deployment strategies configuring model monitoring alerting and troubleshooting prediction service issues.
• Cloud Operations: Implement data processing pipelines in the cloud (e.g. Spark Dataflow) and manage ML services effectively. You will optimize cloud resources for cost-efficiency security and reliability.
• Experimentation & Observability:Design and execute experiments (A/B tests) and create dashboards to monitor model health feature drift prediction distributions and performance SLOs (Service Level Objectives).
• Collaboration & Influence:Translate business needs into technical tasks and build consensus on analytical approaches. You will mentor junior team members through constructive code/methodology reviews and communicate findings effectively to varied audiences.
• Documentation:Produce clear technical design documents and architectural roadmaps for data sources transformations assumptions and experiments to ensure long-term scalability and maintainability.

• Education:Bachelors or Masters degree in Data Science Computer Science Statistics or a related quantitative field.
• Technical Mastery:Proficiency in Python/R and SQL. Advanced experience with DS libraries such as Scikit-learn and exposure to deep learning frameworks (TensorFlow/PyTorch).
• Cloud Proficiency:Hands-on experience with cloud data storage and compute (e.g. GCS Vertex AI) build pipelines andpartnering with engineering to operationalize production-ready models.
• Software Engineering:Strong understanding of Git workflows unit testing and advocating for quality tools/practices in Data Science workflows.
• Business Acumen:Proven ability to partner with stakeholders to define data-driven strategies and quantifiably contribute to business goals via models and insights.
• Communication:Demonstrating strong visualization skills and the ability to socialize model results into strategic implications for leadership covering both technical and non-technical audiences.