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.