Senior Data Scientist, Instanda

• 5+ years of proven experience delivering AI/ML models in production environments
• Advanced knowledge of machine learning, deep learning (e.g., XGBoost, LSTMs, transformers), and statistical modeling
• Proficient in Python and machine learning libraries such as Scikit-learn, TensorFlow, PyTorch, etc
• Hands-on experience with MLOps tools and practices: model tracking, drift detection, continuous learning
• Strong background in data processing, feature engineering, and scalable ML pipelines
• Experience working in cloud environments (Azure preferred) and with containerization tools (Docker, Kubernetes)
• Demonstrated success engaging with senior stakeholders and influencing decision-making with data
• Excellent communication and storytelling skills with the ability to convey complex concepts clearly
• Strong leadership qualities: ownership, initiative, mentoring, and strategic thinking
• Self-starter mindset with a track record of thriving in fast-moving, startup-like environments

Desirable

• We’re looking for a Senior Data Scientist to lead and scale our AI solution efforts. This role is perfect for an experienced professional who has consistently delivered high-impact AI and ML solutions, led cross-functional initiatives, and influenced strategic decisions through data
• You’ll have substantial autonomy, ownership, and a voice in shaping the product roadmap, with significant opportunities for growth and leadership
• Independently design, develop, and deploy robust, scalable AI/ML solutions tailored to business needs
• Lead the full model lifecycle: problem formulation, data exploration, model development, validation, deployment, and monitoring
• Collaborate with engineering and product teams to embed models into production systems and deliver end-to-end solutions
• Act as a technical leader and mentor within the data science team, supporting the growth of junior colleagues
• Identify model/data drift and implement retraining pipelines or adaptation strategies to ensure sustained model performance
• Champion the use of interpretable AI and communicate insights clearly to both technical and non-technical stakeholders
• Engage with senior stakeholders to understand business objectives and translate them into data-driven strategies
• Contribute to and evolve our AI/ML best practices, architecture, and toolchain
• Stay ahead of AI/ML trends and evaluate new tools or methodologies for potential use