
Senior Data Scientist
6 days ago
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.
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