Technologist, Data Engineering
2 weeks ago
We are seeking a highly experienced and visionary Senior Data Scientist at the Technologist level to lead strategic AI/ML and GenAI initiatives. This role demands deep technical expertise, leadership in complex projects, and a passion for innovation in data science and advanced analytics.
Key Responsibilities
- Lead the end-to-end data science lifecycle: problem definition, data acquisition, model development, deployment, and monitoring.
- Architect and implement scalable AI/ML solutions using modern frameworks, cloud platforms, and MLOps best practices.
- Drive GenAI initiatives including fine-tuning, prompt engineering, and integration into enterprise applications.
- Provide strategic direction and thought leadership on advanced analytics adoption across the business.
- Mentor, coach, and upskill a team of data scientists and engineers; foster a culture of innovation and collaboration.
- Partner with cross-functional teams (engineering, product, factory operations, IT) to translate business needs into data-driven solutions.
- Ensure model robustness, fairness, interpretability, and compliance with ethical AI standards.
- Design and oversee experimentation frameworks (A/B testing, causal inference, statistical modeling) for data-driven decision making.
- Stay ahead of emerging trends in AI, ML, and big data technologies; evaluate their potential for business impact.
- Present insights, models, and strategies to senior leadership and non-technical stakeholders in clear, actionable terms.
- MS/ME/MTech/PhD in Data Science, Statistics, Computer Science, or related fields.
- ~15 years of experience in data science, AI/ML, or advanced analytics, including leadership in complex projects.
- Proven expertise in:
- Machine Learning, Deep Learning, and Statistical Modeling
- Optimization techniques for solving complex, high-dimensional problems.
- GenAI applications including architectures like RAG, fine-tuning, and LLMOps.
- Synthetic data generation and handling highly imbalanced and high-volume datasets.
- GenAI applications including architectures like RAG, fine-tuning, and LLMOps.
Experience with anomaly detection, pretrained transformers, and custom embedding models.
Strong proficiency in Python and SQL for data wrangling, analysis, and modeling.
- Hands-on experience with TensorFlow, PyTorch, Pyspark, and related AI/ML frameworks.
- Deep understanding of Big Data platforms (e.g., Spark, Hadoop, distributed databases, cloud data warehouses).
- Experience in MLOps: model deployment, monitoring, versioning, and lifecycle management.
- Strong knowledge of data architecture, pipelines, and feature engineering at scale.
- Familiarity with data visualization tools: Tableau, Power BI, Matplotlib, Plotly.
- Excellent communication and stakeholder management skills, with the ability to influence at senior levels.
All your information will be kept confidential according to EEO guidelines.
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