- End-to-End AI/ML Solution Delivery
- Design & Develop: Design, develop, industrialize, and deploy advanced predictive and machine learning models (e.g., supervised/unsupervised, scoring, NLP, recommendation & personalization systems,classification, regressions and diagnostic).
- Build & Innovate: Build, maintain, and improve robust, scalable data and ML pipelines, from exploration to production (API, CI/CD, MLOps). Construct and innovate with Generative AI systems (LLMs, RAG, fine-tuning) to enhance product features and internal processes.
- Ensure Quality: Guarantee solutions are robust, scalable, and production-ready by applying best practices in software engineering and MLOps (e.g., containerization, monitoring, versioning).
- Own & Deploy: Own your models end-to-end, from idea generation and prototyping to deployment, monitoring, A/B testing, and continuous improvement. Drive the integration of models into production-ready pipelines, establishing and managing version control, validation, and automated testing strategies.
- Technical Strategy & Leadership
- Problem Framing: Frame use cases with business teams, validate data availability, and use rigorous analysis to break down ambiguous business questions into testable hypotheses and concrete data science projects. Define success metrics (KPIs) upfront to ensure every project has a measurable business impact.
- Technology Assessment: Lead make-or-buy decisions by evaluating the trade-offs between building proprietary models versus integrating third-party AI services, always aligning with business objectives for speed, cost, and long-term value.
- Mentorship & Standardization: Act as a technical reference, providing leadership and mentorship to junior data scientists through code reviews and knowledge sharing. Drive technical excellence by standardizing frameworks and improving our technical stack, modeling approaches, and deployment practices.
- Innovation: Stay ahead of market trends and conduct active research on the latest advancements in AI/ML to identify and propose innovative approaches and new use cases. This includes attending conferences,, and participating in meetups and internal learnshops.
- Collaboration & Influence
- Data-Driven Collaboration: Act as the analytical authority within cross-functional teams (Product, Engineering, Business). Challenge existing assumptions with data, guide strategic decisions with analytical evidence, and ensure model outputs are directly integrated into business workflows.
- Insightful Communication: Go beyond just presenting results. Clearly explain the why behind your model's logic, its limitations (e.g., biases, confidence levels), and the trade-offs between different analytical approaches to both technical and executive audiences.
- Actionable Visualization: Create clear dashboards and visualizations that tell a story with the data, empowering stakeholders to make data-informed decisions independently.
- Cross-Functional Leadership: Lead collaboration with IT and Data Engineering teams to enhance data accessibility, harmonization, pipeline robustness, and streamline model deployment.
- Data Strategy: Evaluate data availability, coverage, and quality, proposing pragmatic approaches for calibration, substitution, or enrichment when datasets are incomplete or imperfect.
ProfessionalExperience
We are looking for a candidate who embodies three core strengths:
- Technical & Execution Excellence You are a master builder who ships robust, production-grade solutions.
- After a master degree and PhD , you bring 5+ years of applied experience as a Data Scientist, with a proven track record of taking complex Machine Learning projects from initial POC to full production deployment.
- Your experience comes from a fast-paced, commercial environment (such as E-commerce, FinTech, Online Travel, SaaS, or modern Tech Consulting), where you have successfully built and shipped models for dynamic pricing, demand forecasting, or resource optimization.
- Your technical toolkit is extensive, with an excellent mastery of Python and its core data science libraries (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, pandas, NumPy).
- You have strong knowledge of the full MLOps and Data Engineering lifecycle, including SQL, containerization (Docker), workflow orchestration (Airflow), CI/CD, and model deployment/serving (MLflow, Kubernetes, REST APIs).
- You have a deep understanding of Generative AI architectures and workflows (embeddings, RAG, fine-tuning) and have experience with GenAI/LLM projects.
- You are adept at creating clear, insightful visualizations using tools like Looker, Tableau, or Power BI and applying Agile/scrum methodologies to deliver results iteratively.
- You have hands-on experience working with large-scale datasets in cloud environments (e.g., AWS, GCP).
- Strategic & Business Acumen You think like a business owner and translate complex data into tangible value.
- You have a proven ability to deconstruct ambiguous business problems into a clear analytical framework, defining testable hypotheses and the KPIs that will measure success.
- You demonstrate strong project management skills, taking ownership of the entire project lifecycle to ensure solutions are delivered on time and create measurable business impact.
- You practice statistical rigor, going beyond model accuracy to validate choices, quantify uncertainty, and ensure the reliability and fairness of your results in a real-world context.
- You have led root-cause investigations, using data to diagnose why business metrics deviate from expectations and recommending a clear solution.
- You demonstrate exceptional business impact translation, turning complex model outputs into a compelling narrative that influences leadership-level decisions.
- Leadership & Collaborative Influence You are a natural coach and communicator who elevates the entire team.
- You possess strong leadership and mentoring skills with a genuine passion for sharing knowledge, helping others grow, and fostering a collaborative team environment.
- Your communication skills are excellent, giving you the ability to tell a compelling story with data and distill complex topics into simple, clear explanations for any audience.
- You have a proactive, autonomous, and results-oriented mindset. You are intellectually curious, pragmatic, and thrive in a fast-paced, collaborative setting.
- You have the business acumen to understand complex challenges and translate them into adapted technical solutions.
