Senior Data Scientist - Generative AI

Roles & Responsibilities :

We are looking for a Senior Data Scientist to join the Data Science Client Services team to continue our success of identifying high quality target audiences that generate profitable marketing return for our clients.

We are looking for experienced data science, machine learning and MLOps practitioners to design, build and deploy impactful predictive marketing solutions that serve a wide range of verticals and clients.

The right candidate will enjoy contributing to and learning from a highly talented team and working on a variety of projects.

We are looking for a Senior Data Scientist who will be responsible for:

- Ownership of design, implementation, and deployment of machine learning algorithms in a modern Python-based cloud architecture, with strong emphasis on Generative AI and NLP models.

- Design or enhance ML workflows for data ingestion, model design, model inference and scoring - including workflows for training and serving large-scale transformer models and retrieval/embedding pipelines.

- Oversight on team project execution and delivery.

- Establish peer review guidelines for high quality coding to help develop junior team members skill set growth, cross-training, and team efficiencies.

- Visualize and publish model performance results and insights to internal and external audiences.

- Good to have: PySpark/Spark platforming experience and MLOps/CI-CD for model Masters in a relevant quantitative, applied field (Statistics, Econometrics, Computer Science, Mathematics, Engineering).

- Minimum of 5 years of work experience in the end-to-end lifecycle of ML model development and deployment into production within a cloud infrastructure (Databricks is highly preferred).

- Proven ability to manage the output of a small team in a fast-paced environment and to lead by example in the fulfilment of client requests.

- Exhibit deep knowledge of core mathematical principles relating to data science and machine learning (ML Theory + Best Practices, Feature Engineering and Selection, Supervised and Unsupervised ML, A/B Testing, etc.).

- Proficiency in Python and SQL required; experience with deep learning frameworks (PyTorch / TensorFlow) and NLP/transformer ecosystems preferred. PySpark/Spark experience is good-to-have.

- Ability to conduct a productive peer review and proper code structure in Github.

- Proven experience developing, testing, and deploying various ML algorithms (neural networks, XGBoost, Bayes, and the like); strong experience with transformer/LLM architectures and prompt/chain-of-thought engineering desirable.

- Working knowledge of modern CI/CD methods and MLOps practices.

(ref:hirist.tech)