- A degree (MSc or Ph.D.) or equivalent in Computer Science, Physical Sciences, Applied Math, or similar. PhD preferred.
- 4+ years of experience in data science, business intelligence, analytics, or academic research
- Strong knowledge of statistics (e.g., Bayesian inference, Bootstrap, hypothesis testing, confidence intervals, maximum likelihood, Monte Carlo).
- Strong programming ability in Python
- Strong experience using the PyData stack for analytics/machine learning, and especially scikit-Learn, pandas, Numpy, MatPlotLib, SciPy
- In-depth knowledge and hands-on experience in machine learning algorithms: random forest, gradient boosted trees, neural networks, k-means, etc
- Experience in full life-cycle of a data science project, from data collection, EDA, model building to deployment
Bonus points for:
- Degree (MSc or PhD ) in Machine Learning or Applied Statistics
- Any hands-on experience in mathematical optimization (esp. linear programming), reinforcement learning, deep learning, or natural language processing
- Success in data science competitions, such as Kaggle and KDD Cup.
- Experience at profiling and increasing the speed of your Python code (e.g., with Cython, Numba, PyPy).
- Contribution to open source projects
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