Senior Data Scientist

3 - 5 Years

About the Role
We are seeking a Senior Data Scientist who can leverage advanced analytics, machine learning, and
NLP techniques to drive data-powered innovation at TurboHire. You will work across product,
engineering, and business teams to design scalable models, solve complex problems, and contribute
to the development of intelligent recruitment solutions.

Responsibilities
 Use expertise in Python, NLP, machine learning, and problem-solving to design and deploy
data science solutions.
 Collaborate with product, marketing, and engineering teams to leverage data science in
solving cross-disciplinary problems.
 Develop and implement analytical and predictive models for product and service
development.
 Build tools and automation pipelines to optimize business processes.
 Mentor and coach junior data scientists, ensuring best practices in methodology, coding, and
experimentation.
 Work closely with ML engineers and software developers to productionize models with
scalability and robustness.
 Conduct in-depth data analysis to derive insights that guide decision-making and product
strategy.

Requirements
 Bachelor’s or Master’s degree in Engineering, Data Science, Statistics, Applied
Mathematics, or Computer Science.
 3+ years of experience in data science or applied ML roles.
 Strong programming skills in Python (experience in Go is a plus).
 Hands-on experience with machine learning frameworks (e.g., scikit-learn, TensorFlow,
PyTorch).
 Familiarity with Flask/Django and database technologies (MySQL, PostgreSQL, or
equivalent).
 Knowledge of data visualization, statistical modeling, and ML algorithms.
 Experience developing and managing data pipelines for machine learning systems.
 Exposure to GPU-accelerated workloads in production environments.
 Excellent communication skills and ability to work collaboratively in cross-functional teams.

Preferred Skills
 Experience applying NLP techniques for text classification, parsing, or semantic search.

 Familiarity with ensemble methods, optimization techniques (adaptable across domains).
 Experience with vector databases (FAISS, Pinecone, Weaviate, Milvus) and embedding-
based retrieval.