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Five Good Uses of Data Science in Products

There was a period, not all that long ago, where startups pitched themselves first as a machine learning or artificial intelligence company, using these technologies to solve complex problems and provide a unique user experience. Now, data science methodologies are much more ubiquitous, that for many new companies and products in specific sectors, to even think about not leveraging them would be heretical.

We all interact with data science daily in the products we use. Like any well-implemented product feature, it blends in seamlessly with the user experience. As a user, you don’t need to know what technology is running in the background of the products you use. You want them to solve your headaches, or provide you joy.

Here is our list of five good uses of machine learning and data science in products,


The thing that always turned me off to shopping for food online is that there is a flow to a supermarket or grocery store. You walk through the various aisles, and the food on the shelves speak to you, catch your attention, make you think of a recipe that you want to try. You may start with a list, but you always end up finding something new that you want to try out.

Ocado is one of the leading employers of data scientists and engineers (in fact our data scientists Jeremy and Johan hail from Ocado). AI and machine learning underpin all of Ocado, including factory layout, driver logistics, customer feedback analysis, responding to customer complaints, and the shopping experience. Ocado technology also helps users to navigate through their shopping more efficiently, having the right next product suggested to them to help them get their shopping done better and quicker. Or, more cynically, so you buy more.

Smart Compose in Gmail

I am a nervous emailer. I’ll often write something and go over it three or four times, changing tiny details, because it doesn’t sound right to me. That all changed when smart compose came around. Somehow, the machine predicting what I should say gave me more confidence to say it.

While that might not be the exact use case or problem to be solved when they started building the product, it does make it one of my favourite features of G Suite. I’d imagine for many power users, and people who live in their inbox, it presents a considerable amount of time savings.

When I first came across this feature, I thought the UI would be a bit awkward, as you have to hit tab to utilise the suggestion. However, in my experience, it fits in quite nicely with how I type. And now as I tap this blog post draft out in Google Docs I wonder when they will bring this to other parts of the G Suite.

The tech behind Smart Compose is pretty impressive. There are many challenges the Google team needed to overcome, including speed (it needs to suggest quicker than people can type after all), scale (providing the right predictions for a given user), and reducing bias in the suggestions.

It uses neural networks to take into account contexts, such as email subject and prior correspondence, and predict what the next phrase might be. They have an excellent blog writeup here on the technology.

Face Grouping in Google Photos/Other Photo Services

This post might give me away as a Google product power user. I love the facial grouping of Google photos. It makes finding the right picture of people, in a sea of the millions of photos we all have on our phones, super quick. I am always impressed by how well it groups people, particularly with my kids. The technology can connect their newborn photos with them as a toddler, even as I struggle to remember” is that Frankie or Archie in this one?” It can also distinguish my cat from the many other cat photos I have on my phone (don’t ask).

This facial recognition technology used across product and features within Google, and they allow developers to deploy the technology in their products, for instance, with the Firebase ML Kit.

Spotify Song Recommendations

I recently switched from the Google Play streaming service to Spotify (see, I can use non-Google products). One of the reasons it took me so long to do so was the headache of having to build a whole new library of music in Spotify. I didn’t want to go through it all and follow my favourite artists. What really surprised me when I made the move was how quickly, and how little data was actually required for Spotify to fairly accurately understand my musical tastes and actually start suggesting to me artists and songs that I frequently listened to on Google Play.

There are a few technologies and techniques Spotify uses to predict your musical tastes and create your tailored playlists. First is collaborative filtering, which makes recommendations to you based on crossover with other listeners with similar preferences. Spotify also uses natural language processing (NLP) and scours the internet, and tags songs based on how frequently they are mentioned alongside other artists and songs. The third method is raw audio processing and recommending similar songs based on like tempos, key and signatures. (more on these methodologies here).

Wealthfront ‘roboadvisor’

Financial services is an area ripe for the application of machine learning and other data science techniques. The vast amounts of available data, along with the inefficiencies, fraud, waste and high fees, make it particularly exciting as a wave of financial technology startups turns the space on its head.

My favourite consumer application in this area thus far is Wealthfront. It automatically builds users a balanced portfolio of exchange-traded funds based on risk profile. It even rebalances your portfolio for you to maximise efficiency. They have also released new features to help with financial planning, such as helping set budgets for when you want to buy a house, start a family, make large purchases, even plan to take an extended holiday. It plugs in all financial accounts you have, your current portfolio and risk preferences, and market data to help you prepare.

Wealthfront’s model allows more consumers to have access to financial planning, advice and portfolio management for significantly lower fees. Previously you would have to pay financial advisors to help you budget, and generally, they require clients to have a minimum net worth. To manage a balanced portfolio, you’d have to either do it yourself, and pay fees to whichever account manager you had, and also have to remember to rebalance your portfolio, and change it as your risk profile changes. Instead, automation, data and machine learning helps you accomplish all this at a fraction of the cost.

Senior Data Scientist

Perth, Australia or London, UK (Full or Part time)

Data Mettle is recruiting a Senior Data Scientist. You’ll be joining our small but highly experienced Data Science team. You’ll get to work on a variety of exciting and stimulating projects with clients ranging from startups to large corporates and government.

About Data Mettle

We are a growing data science consultancy, and are working to establish ourselves as a leader in this area. We have a diverse client base so our team get to work on a variety of interesting and complex data projects. Our clients are our partners; we listen to them, again and again. We work with them in an agile way to facilitate collaboration, continuous feedback and learning. We’ve recently expanded into Australia. We are excited to be building both of our London and Perth teams, and have some new data scientists join our journey.

What we are looking for

  • Master’s or PhD degree in a highly numerate subject, for example machine learning, statistics, bioinformatics, physics or applied mathematics.
  • Solid understanding of statistical methods ideally including experience with optimisation and machine learning methods.
  • Expertise in programming languages such as R or the Python scientific stack (NumPy, SciPy, scikit-learn, etc.).
  • Experience with cloud computing and infrastructure (we work flexibly with Google cloud services, AWS and Azure) and SQL.
  • An ability to understand customer requirements, how our solutions will work in situ and how they will be successfully embedded to deliver value.
  • A natural collaborator, who will partner with our clients and be confident sharing their knowledge in client meetings, workshops and to the wider community.
  • An inquisitive self-starter, with strong analytical, critical thinking, and problem solving skills and the ability to constantly learn and teach others.
  • We’re hoping to find someone with strong personal accountability for results, who enjoys working with different clients, often on their sites and is comfortable operating in a startup environment.

Responsibilities will include

  • Work with clients to solve diverse business challenges across a wide variety of areas. Providing technical leadership and managing relationships with clients and their teams.
  • Apply quantitative techniques to cleanse and explore large (and small), complex data sets in preparation for further analysis.
  • Assure high quality implementation of solutions, as well as providing training and knowledge transfer to client teams.
  • Stay current with new data science methods, technologies, and industry trends.
  • Work with the team to develop and implement data science processes and best practices.

Why You Should Apply

  • Competitive salary and annual leave entitlements
  • Flexible working arrangements
  • Annual training budget and other benefits
  • Working with a wonderful team of like-minded data scientists

To apply, please email your CV to jobs@datamettle.com

We are an equal opportunities employer, we recruit by merit on the basis of fair and open competition and we welcome applicants from all backgrounds.

Concentre Consulting uses Data Mettle to Automate Human Checks

Concentre Consulting manually verify metadata of construction project documents such as drawings and schematics. This involves checking the alignment of external and internal metadata. It is a time-consuming process, involving about 200 checks per day per person. Data Mettle built a tool that automated this metadata verification, which included a complex method of verifying text that was embedded within diagrams. Data Mettle’s work unlocks potential time savings of 90%. In addition, it improves the quality of checking, providing a consistent approach that would not be possible with multiple people.

About Concentre

Concentre Consulting focuses on digital transformation in the built environment. They work with large construction companies on skyline-changing projects, ensuring the right information is available to the necessary stakeholders when they need it.

The Challenges

Managing large-scale construction projects is an enormous administrative undertaking. One of the many elements of this you might not consider when looking at a skyscraper is that file naming structure has a critical role to play. Contractors and subcontractors need to know which schematics and drawings are associated with their current tasks. Is this the right document for the layout on this particular floor? Is this the latest version? Who uploaded it to the database, and who needs it? The way they do it is through the file naming and metadata structure, where each element corresponds with necessary information about the document. Which floor does this drawing represent? What version are we on? Standardised file naming and forcing consistent metadata systems allows stakeholders to quickly identify what that file is for and what version it is.

However, this task is done by humans, which is tedious, slow and subject to error. Misnamed files could lead to delays or worse. One of Concentre’s jobs is to quality assure the file naming structure of these critical documents, checking that the file name corresponds to the metadata contained in the drawings and schematics. They did this using human oversight – checking the metadata against the filename and the corresponding convention for each project.

The challenge was how to automate this so that we could not only speed up this time-consuming process but importantly improve accuracy.

The Solution

Data Mettle was able to build a tool that did just that. Using natural language processing, the tool scans the pdf documents uploaded by stakeholders. It extracts data such as building floor, file type, and other identifying information used in the file naming. It would then verify whether the file was named correctly. And it would do it instantly and without human oversight.

The Results

This tool can present Concentre Consulting and their clients with significant cost savings. A team member would spend 8 hours doing 200 checks. They can complete the same amount of checks in just 30 minutes, representing an efficiency increase of over 90%. In the next phase of the project, Data Mettle will work with Concentre to build the capability to deliver the tool in the cloud so the benefits can be realised.

Have a problematic process you want to automate like Concentre? Get in touch with us.

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