Its fascinating to hear the range of techniques and skills, used by analysts or data scientists, these days.
We place candidates across a range of sectors. Now it seems even the more conservative are becoming sophisticated in using analytics.
Take Banking for example. Thats normally thought of as a traditional sector; slow to change. But analysts & data scientists have told us about a growing range of techniques theyre using and new knowledge theyve gained.
Where Banks Have Come From
Looking back, a decade ago:
- MBN was still a young business & new to this field;
- Companies were looking for maths graduates or just highly numerate grads;
- Banks said they just needed data analysts to profile customers or produce data lists for mailing campaigns;
- A few of the more advanced were building propensity models;
- Analysts reported that their jobs were mainly about leads to increase sales;
- All the banks seemed to use SAS and these skills were growing in demand.
Nowadays that feels like a different world. The growth of the internet, smartphones, mobile banking and digital payment have changed all our lives. Plus, the banking sector has had its scandals and basically lost the trust of most customers. So, its not surprising that we hear a very different place of work 10 years on. It sounds like most major banks now have much more focus on their customers, improving their cultures and using data with analytics to automate better service and marketing.
Todays Banking Analytics
Today, when we are asked to find candidates for major Banks, its more likely that well be asked for graduates with specific skills in data science. Many recruiting managers are setting a higher bar of technical competence.
The most advanced will ask for:
- Data visualization skills (including interactive);
- Knowledge of Data Science coding languages (R or Python), although most clients still use SAS;
- Digital analytics experience (Google Analytics is just the basics);
- Evidence of internships or other experience with real world data;
- Statistical skills, even including mention of Bayesian logic, whatever that is?
If you think that is a far cry from the old style reporting days of analytics within banks, the challenges these candidates are tackling are even more impressive. Without betraying any confidences, weve been impressed with how interesting the analytical work can be, within todays major banks.
Analysts tell of:
- Dynamic pricing models, varying price by individual circumstances;
- Lifetime Value Models that are re-scored in real time during a conversation;
- Clustering of customers according to financial needs & behaviour;
- Optimizing & coordinating all contact with a customer, across all channels & products;
- Risk models that consider a much wider range of individual data;
- Connecting social media sentiment with digital behaviour & geo-location to guide personalized mobile offers.
A New Career Opportunity?
Phew, a far cry from the rather dusty tired old image of traditional banking. From what we can see, this is a sector thats working hard to pick up best practice from others. Retail, Pharma & Telco used to be the more advanced sectors. Those to which we would direct candidates wanting to focus on their technical skills or statistics.
But these days, it seems there is plenty of opportunity to be a valued data science within banks. In fact, given the growing focus on innovation and use of predictive analytics, means analysts from other sectors are in demand. If youve developed experience in digital marketing analytics, or optimizing customer interactions in other sectors, you could find banking a great opportunity. They have billions of rows of data and investment in the tools needed.
