Data Scientist
Role details
Job location
Tech stack
Job description
We are looking for a Data Scientist - to adept at designing and implementing predictive algorithms, knows how to lead projects with a results-driven approach, taking an idea from a business question all the way to a working solution in production.
If you're looking for a place that offers meaningful challenges, long-term stability and room to develop your career in AI and data science, this is it.
What you'll do?
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Own the full project lifecycle end to end, from business needs to research questions and POCs, through development, deployment, monitoring, and maintenance
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Develop, test, and deploy machine learning models with a focus on time series forecasting and predictive analytics
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Research and build LLM-powered applications for personalization, recommendation systems, and internal productivity tools
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Work with text embeddings, feature engineering, and RAG architectures
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Refine prompts and evaluate LLM performance to ensure accuracy, safety, and consistency
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Build robust data pipelines for structured and unstructured data
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Establish and run A/B testing frameworks to support data-driven decisions
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Partner with product, engineering, and business teams to present findings clearly and drive action
Requirements
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2 years of industry experience in Data Science with actual production,working with Machine Learning, or a related field- Must
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B.Sc./M.Sc. in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field
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Strong proficiency in Python - Must
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Hands-on experience with ML frameworks such as scikit-learn, Pandas, CatBoost, and XGBoost
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Basic knowledge of SQL for data extraction and manipulation
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Proven experience with predictive modeling and A/B testing methodologies
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Solid understanding of model training, validation, and statistical analysis
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Strong analytical skills with genuine attention to detail
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Demonstrated ownership and accountability for your work
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Exceptional independent learning capability and a proactive mindset
Advantage - Nice to have
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Hands-on experience with LLMs, including fine-tuning, embedding pipelines, vector databases, and RAG
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Experience with NLP tasks such as summarization, entity extraction, text classification, or semantic search
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Experience with GCP, specifically BigQuery and GCS
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Familiarity with Docker and containerization
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Experience deploying models to production environments
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Familiarity with dbt for data transformation
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Understanding of responsible AI principles and model evaluation frameworks