Senior Data Scientist

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.