Description :

Data Scientist

Experience : 8 to 15 Years

Industry : Technology / Data-Driven Products

Education : Masters or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.

Role Summary :

We are seeking a high-caliber Staff Data Scientist to lead the intersection of statistical research and scalable engineering. In this senior leadership role, you will act as a "Technical Architect of Intelligence," responsible for designing, validating, and deploying end-to-end machine learning solutions. You will bypass "notebook-only" modeling to build robust, production-grade systems that solve high-stakes business problems. The ideal candidate is a Python mastery expert with deep roots in Deep Learning frameworks and MLOps, capable of mentoring junior talent while translating high-dimensional metrics into strategic stories for executive leadership.

Responsibilities :

- Advanced Model Engineering : Design, train, and optimize sophisticated machine learning models across supervised, unsupervised, and deep learning domains using PyTorch, TensorFlow, or JAX.

- End-to-End MLOps Orchestration : Architect and maintain robust automated workflows for model training, continuous evaluation, and deployment using Docker, Kubernetes, and MLflow.

- Feature Engineering at Scale : Extract and transform complex variables from massive, high-dimensional datasets to maximize predictive power and model accuracy.

- Statistical Experimentation : Design and execute rigorous A/B testing and multivariate experiments to validate model impact on core business KPIs and user behavior.

- Stakeholder Storytelling : Translate complex model metricsincluding Precision-Recall curves, F1-scores, and ROC-AUCinto actionable business narratives for non-technical leadership.

- Big Data Processing : Utilize distributed computing frameworks like Apache Spark (PySpark) to process and analyze petabyte-scale datasets for feature extraction.

- Production-Grade Coding : Champion software engineering best practices by writing maintainable, modular Python code and leading rigorous peer code reviews.

- Cloud Infrastructure Leadership : Deploy and manage models within major cloud environments using services like AWS SageMaker, GCP Vertex AI, or Azure ML.

- Technical Mentorship : Act as a force multiplier by coaching junior and mid-level Data Scientists on experimental design and production deployment strategies.

Technical Requirements :

- Senior Domain Expertise : 8 to 15 years of professional experience in Data Science, with a proven track record of deploying models into production.

- Programming Mastery : Expert-level proficiency in Python (Pandas, NumPy, Scikit-learn) and advanced SQL for complex ETL and data extraction.

- Deep Learning Frameworks : Hands-on proficiency in PyTorch or TensorFlow and gradient boosting libraries like XGBoost or LightGBM.

- Containerization & Tracking : Extensive experience with Docker/Kubernetes and experiment tracking tools like Weights & Biases or MLflow.

- Cloud-Native ML : Strong working knowledge of SageMaker or Vertex AI.

Preferred Skills :

- Research Contributions : Proven experience in implementing papers from NeurIPS, ICML, or KDD.

- Advanced Orchestration : Familiarity with workflow tools like Airflow, Kubeflow, or Prefect.

- Vector Databases : Experience with Pinecone, Milvus, or Weaviate for LLM/RAG use cases.

Core Competencies :

- Strategic Influence : Ability to align ML initiatives with long-term product roadmaps and business goals.

- Analytical Rigor : A commitment to scientific precision and statistical validity in every experiment.

- Collaborative Leadership : Excellence in navigating the intersection of Product, Engineering, and Research teams.

- Result Driven : A focus on moving beyond "experimental" metrics to realize tangible business value.