Data Scientist at Zopa
London, GB
About Zopa
Zopa is radically transforming the way consumers borrow and save money. We are the leading peer-to-peer lending company in Europe, delivering better value than traditional banks. More than 63,000 people have lent over £1.8 billion through Zopa since our launch in 2005, and we are regularly featured in the press as an innovator in our sector. We've been voted 'Most Trusted Personal Loan Provider' in the Moneywise Customer Awards for the past 6 years in a row.

The role
Data Science at Zopa is hands-on and quantitative. Our Data Scientists propose ideas, formulate them as hypotheses, test them with statistics, and present insights with actionable information across a broad range of topics, such as risk, marketing, fraud, pricing, and operations. Working within a multi-disciplinary team using the latest technologies in analytics and big data, a Data Scientist at Zopa will have the programming skills to go beyond what the tools can do.
Requirements

About you
You are detail oriented and obsessed with data quality. You are strong in transforming and modeling data at scale, machine learning, and statistics. You have good business acumen and are interested in how companies operate and create revenue. You have an inquisitive mind, are intrinsically curious and are passionate about deriving insights from data.

Essential skills/knowledge:
A degree (MSc or Ph.D.) or equivalent in Computer Science, Physical Sciences, Applied Math, or similar
2+ years of experience in data science, business intelligence, analytics, or academic research
Strong programming ability in Python
Experience using the PyData stack for analytics/machine learning, and especially scikit-Learn, pandas, Numpy, MatPlotLib, SciPy
SQL
Linux/Unix
Bonus points for: ​
Strong knowledge of statistics (e.g., Bayesian inference, Bootstrap, hypothesis testing, confidence intervals, maximum likelihood, Monte Carlo).
Experience with mathematical optimization (esp. linear programming)
Experience adding value from data in a business setting.
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).
Experience using Hadoop (HDFS, Hive, HBase, Spark, MLLib)
Knowledge of deep-learning fundamentals, and preferably real-world experience at developing deep-learning models (e.g., with Theano, TensorFlow, or Keras).