- Company Name
- Reward
- Job Title
- Applied AI Scientist
- Job Description
-
**Job Title:** Applied AI Scientist
**Role Summary:**
Develop, prototype, test, deploy, and optimise AI/ML solutions that generate commercial value. Lead end‑to‑end AI delivery cycles, experiment with emerging tech (LLMs, Agentic AI), and mentor junior talent.
**Expectations:**
- Deliver production‑ready ML models (forecasting, segmentation, attribution, uplift, recommendation).
- Drive measurable business outcomes by translating business problems into AI solutions.
- Maintain high engineering standards, monitoring, and reproducibility.
- Foster cross‑functional collaboration and AI adoption across the organisation.
**Key Responsibilities:**
- Design and prototype advanced ML models for forecasting, segmentation, attribution, uplift, and personalization.
- Create and maintain end‑to‑end ML pipelines (data sourcing, feature engineering, training, evaluation, deployment, monitoring).
- Build production‑ready pipelines on AWS (SageMaker, Bedrock, Redshift, S3) and integrate models into production systems.
- Own CI/CD, versioning, model governance, and AIOps practices.
- Lead experiments, PoCs and research on Agentic AI, GenAI workflows, and LLM automation.
- Collaborate with analytics, product, marketing, and commercial teams to embed AI into customer experiences and internal tools.
- Mentor junior analysts in Python, statistical methods, experimentation, and model development.
- Introduce new techniques, tools, and frameworks to accelerate AI capability.
**Required Skills:**
- Proven experience delivering production AI/ML models.
- Proficient in Python, SQL, and popular ML frameworks (PyTorch, TensorFlow, Scikit‑Learn).
- Hands‑on with AWS AI/ML services (SageMaker, Bedrock, Redshift, S3).
- Deep knowledge of forecasting, segmentation, personalization, uplift modelling, and attribution.
- Experience with CI/CD, model versioning, AIOps, and automation workflows.
- Strong experimental design, statistical thinking, and storytelling to non‑technical stakeholders.
- Ability to prototype quickly while ensuring code quality and reproducibility.
**Required Education & Certifications:**
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, or related field.
- Certifications in AWS Machine Learning / Data Analytics, or equivalent, preferred.