At deepsense.ai, you won’t just build AI solutions – you’ll shape how companies around the world use them.
By joining us, you’ll:
- Work with partners like OpenAI, NVIDIA, Anyscale, LangChain, Crusoe, and ElevenLabs.
- Explore and apply the newest tech: LLMs & RAG, MLOps, Edge Solutions, Computer Vision, Predictive Analytics.
- Tackle challenges in software & tech, pharma & healthcare, manufacturing, retail, telecoms & media.
- Contribute to open-source projects – just take a look at our latest solution, ragbits, an agentic RAG framework with over 1.6k stars on GitHub.
And the best part of working at deepsense.ai?
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Spread your wings with clear career paths, technical or leadership.
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Collaborate with 100+ AI experts with 15+ years of applied AI experience, as well as PhD-level researchers with academic backgrounds.
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Tap into domain expertise and knowledge sharing whenever you need it.
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You’re closest to the data and analytics from exploration and cleaning, through modeling, to generating business insights.
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Your tools include classical and non-linear ML models (regression, XGBoost, LightGBM, CatBoost) and libraries such as pandas and scikit-learn.
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You create analyses and visualizations that directly support business decisions.
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You have a real impact on client strategies and decisions, your models and analyses don’t end up in a drawer.
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Projects are diverse from EDA and feature engineering, to predictive modeling, time-series analysis, data visualization, and dashboard development.
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You get room to grow, whether into deeper data analytics and business consulting, or towards AI engineering (working closely with MLEs and SEs).
The ideal candidate:
- Hss a minimum of 3 years of experience in Data Science, delivering end-to-end, data-driven solutions.
- Is proficient in classical ML techniques (linear regression, feature selection methods, predictive modeling, time series) as well as non-linear methods (gradient boosting, random forest, XGBoost, LightGBM, CatBoost).
- Programs fluently in Python and uses libraries such as NumPy, pandas, scikit-learn.
- Can effectively manage and analyze data using SQ L.
- Creates clear and engaging visualizations (matplotlib, seaborn, plotly).
- Can translate data into actionable business insights and recommendations.
- Stands out with strong communication skills and the ability to explain complex concepts in a simple way.
- Bonus points for experience in data engineering and cloud platforms (AWS, GCP, Azure), as well as knowledge of dashboarding tools (Tableau, Power BI, Dash) and experience in NLP or leveraging LLMs/Generative AI.
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