Senior Data Scientist

About The Role

We are seeking a Senior Data Scientist who can leverage advanced analytics, machine learning, andNLP 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 contributeto the development of intelligent recruitment solutions.

Responsibilities

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

Requirements

Bachelor’s or Master’s degree in Engineering, Data Science, Statistics, AppliedMathematics, 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, orequivalent).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.