22nd May 2018
£400 - £600
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At Symphony-APS, we are relentless in our pursuit of new ways for the Accounting profession to help their clients. We do this by providing our clients with technical platforms which deliver actionable insights.
We’ve been in existence for 20 years and work with major accounting practices in the UK, USA and Australia.
The next stage in our evolution is all about Data Science and using Predictive Analysis to point our clients to areas where they can add the most value.
We want our team to propose ideas, formulate them as hypotheses, test them with statistics, and present actionable insights across client churn, client expansion opportunity, marketing, cashflow forecasting and operational performance.
You will be a key part of the team based in our offices in Purley, working alongside experts in the business domain, in programming and in the various tools used within the Accounting sector.
Your key outcome will be to produce replicable validated routines which we can share across our existing and future client base to improve performance in client retention, client product extension, business intelligence and fraud detection.
This is initially a 6-week project, minimum 30 hours per week, based in our office in Purley. We feel strongly that this team needs to work closely together and in order to achieve the speed we’re looking for, you’ll be here on at least 3 days of the week.
You are detail oriented and obsessed with data quality. You are strong in transforming and modelling data at scale, machine learning, and statistics. You have good business acumen and are interested in how companies operate and create revenue. You have a relentlessly inquisitive mind, passionate about deriving insights from data. You’ve got the confidence to follow your own intuition as well as the wisdom to learn from your team members’ experiences.
- A degree or equivalent in Computer Science, Applied Math, Statistics or similar;
- 2+ years of experience in data science, business intelligence or analytics;
- Strong knowledge of statistics (e.g., Bayesian inference, Bootstrap, hypothesis testing, confidence intervals, maximum likelihood, Monte Carlo).
- Sound programming ability in Python or R;
- 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
- Expertise on Knime
- 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.
We are committed to equality of opportunity for all staff and applications from individuals are encouraged regardless of age, disability, sex, gender, sexual orientation, pregnancy and maternity, race, religion or belief and marriage and civil partnerships.