Data Scientist

Our Transformatics team builds data and AI products to provide analytics insights to clients and teams involved in transformation programs across the globe. The current team is composed of data engineers, data scientists and product managers who are spread across several geographies. The team covers a variety of industries, functions, analytics methodologies and platforms – e.g. Cloud data engineering, advanced statistics, machine learning, predictive analytics, MLOps and generative AI.

What You’ll Do

You will collaborate closely with a team comprising data scientists, data engineers, product developers, and analytics-focused consultants.

You will work on topics such as descriptive analytics, predictive models (e.g., boosted trees), and large language models (LLMs), particularly for segmentation use cases. Additionally, you will design and deliver products that adhere to MLOps best practices, ensuring they are both maintainable and deployable. By doing so, you will help bring advanced analytics capabilities into one of flagship products, named "Wave". Your work will be the backbone for how client runs future Transformations, leveraging data science assets, to improve the odds of success for our clients.

In this role, you will be involved in the following areas:

● Advanced insight generation: transforming complex business questions into statically relevant analyses and these analyses into easy to digest insights.

Delivering this through well documented and tested pipelines, that allow easy collaboration with other team members.

● Upholding technical excellence: Together with the tech lead(s) define how to build, maintain and scale our pipelines. Often piloting new technical approaches & automation.

● Using your business understanding to critically review model results, trends, analyses and classifications.

● Coach & help other colleagues: Coach & help you peers when needed, we deliver as a team.

● Machine learning model development: Lead the design and refinement of statistical models, optimization techniques, advanced machine learning, and predictive models

● LLM optimization and evaluation: Fine-tune and evaluating the performance and efficiency of Large Language Models, leveraging the latest advancements in neural network architectures and machine learning techniques

Qualifications

● Bachelor's degree in the field of computer science, machine learning, applied statistics, mathematics or related field required. Advanced degree is a strong plus

● Strong data translation and presentation skills with the ability to clearly and effectively communicate complex analytical and technical content

● 3-6 years of relevant experience with classical descriptive statistics, standard statistical modelling (e.g. advanced regressions, clustering, classification models) and machine learning techniques (e.g. random forest, support vector machines, gradient boosting, XGBoost)

● Programming experience in the following languages: Python and SQL

● Experience in leading complex engagements to deploy advanced analytics and data science methods at scale in real world organizations

● Exposure to extra tooling such as Snowflake, Excel and Tableau is a plus

● Familiarity with neural network architectures (Transformers), RAG models, deep learning libraries (TensorFlow/PyTorch) and machine learning libraries (scikit-learn) is a plus