Senior Big Data / ML engineer

Senior Big Data/ML engineer

What will be your impact?

AI/ML enables the creation of superior performing organizations. What these digital and AI/ML enabled organizations exactly look like is unknown. Innovation, creativity, perseverance and digital business & AI/ML technology craftsmanship will create them. We are looking for a senior big data / ML engineer that will push the boundaries for our customers. 

Your role

Your role as Senior Big Data/ML Engineer is to demonstrate clients the art-of-the-possible and apply AI/ML technologies to create business benefits. You will be working with clients to define their AI and Data roadmaps, improve their capability, but foremost you will be realizing and scaling their state-of-the-art cases. Use cases could be for example the introduction of voice enabled chatbots, text analytics to classify emails or documents, or a digital twin to reduce waste of a production process.

Typically you will apply data collection, scaling AI/ML models and building data pipelines while working in multidisciplinary teams. The Google Cloud Platform provides our preferred technology stack. Essential for success will be your seamless cooperation with the digital business consultants, data scientists and software engineers from Digital Sundai, our network partners and clients. Next to being a great engineer we expect you to be a strategic innovator of our services.


At Digital Sundai based out of Amsterdam. A young and ambitious start-up.

Digital Sundai strives to create superior organizations through Digital & AI. We believe Digital & AI projects only succeed when technology & business are both done right. We bring experienced digital business competence and top AI & Analytics expertise. Executed through our agile digital culture and methodology. Digital Sundai is a networked enterprise which only works with top digital talent & top digital partners. We are an Open company and an integral part of the Digital community with relations and access to the latest Business & Tech start-ups, scale-ups, academia, and established companies. Google Cloud is our preferred Technology ecosystem

Working in creative, agile teams, you’ll help clients discover, design and unlock the digital opportunity for their organizations. You’ll help them modernize their data and analytics environments, and apply next generation AI/ML technologies. These are technologies driven by the transformational impact of AI-enabled, automated processes and optimized through human-centered design.

What we offer

  • a fixed salary.
  • employee ownership of the company (shares) as we believe in entrepreneurship.
  • flexible working hours and the opportunity to work from home.
  • a good pension scheme.
  • 25 days of paid holiday annually, and the opportunity to purchase additional holiday days annually.
  • a 32- 40 hour working week.
  • the opportunity to take unpaid leave.
  • a good mobility policy allowing you to lease a car, use public transport or get reimbursed for travel costs.
  • a laptop and a phone,  which are also for personal use.
  • great growth opportunities. Depending on your ambitions and performance, you can grow your impact very fast, and reap the benefits as co-owner of the company.

What you offer

  • Academic Master Degree, preferably in Information Sciences, Data Science or Analytics. Additional experience at digital service providers is making you an even better fit.
  • >4-6 years of relevant experience as a Big Data / ML engineer with a track record of many successful AI/ML & Analytics implementations.
  • Digital technology savvy and with a passion for AI/ML & engineering. And you believe as us that this is the ultimate professional playground for years to come.
  • Ambition to gain Expert knowledge in leading AI & analytics tools such as Tensorflow, Hadoop, NoSQL, Kubeflow, and modern SW technology like docker and  CI/CD tools. Good working knowledge of Python and R.
  • GCP certification like Professional Data Engineer.
  • Experience in supervised, unsupervised and reinforcement learning approaches, designing and building data processing systems, designing, building and operating machine learning models and ensuring solution quality.
  • A digital business mindset 
  • You are ambitious, curious and entrepreneurial
  • You are a team player and you have a proven record functioning in multidisciplinary teams
  • Knowledge of Dutch and English

Digital transformation is not about technology – call to share experiences

Successful Digital transformation is not about technology. Sure, you need to get the tech right but that’s not where most organizations fail. Getting the business right, the employees on board, the digital product right and all of this at speed…… that’s where it is really hard.

How did you do it? Please share your experiences in the comments below.

Bootstrapped digital transformation

Tomorrow delivered today is the tagline of Digital Sundai. At Digital Sundai we deeply believe that applying digital and AI technologies enables a far better performance of organizations. And we mean today’s digital technology, no need to wait on the even better technology of tomorrow. We also think few organizations and industries have already truly leveraged these benefits. 

At the moment the disruption of Covid-19 is accelerating the adoption of digital technology at unprecedented speed. Whether it is online shopping, remote digital education, call centres with all agents working from home, AI to detect Covid-19 from CT scans or apps to support governments to control the spread of the virus. Habits are changing and will not return fully to pre-Covid 19 days. At the same time the upcoming recession will make cost reduction a key theme for almost all organizations. This will severely limit the ability of organizations to invest in digital change. 

So here we have our dilemma. Society and markets will reward the digitally mature organizations more than ever, yet organizations will have less means to invest to become digitally mature. The answer in our view….bootstrapped digital transformation. 

Sounds good bootstrapped digital transformation but is it possible to realize digital change at low cost in existing organizations? Most research on why digital and AI are difficult to scale in organizations list several topics like strategic focus, lack of skilled resources, legacy technology, which will not change a lot in the coming period. The opportunity however lies in what most research list as key blockers;

  • Organizations are not bold enough when pursuing digital transformation
  • The way they are organized and their culture do not fit the digital age. 

Recently Covid-19 has forced organizations to be bold and to change ways-of-working to digital overnight, and in most cases successfully. The focus on the job to be done was clear, and the lack of resources nor legacy technology did stop organizations from realizing the changes. Also, compared to making these kinds of digital changes under normal business circumstances the costs were multiple times lower. 

Bootstrapped digital transformation keeps this momentum going, without the disruption that originally enabled it. Key for bootstrapped digital transformation:

    1. Top management has a clear focus for what areas to digitize. This directs the efforts of the organization.
    2. A little more action, a little less conversation please. Probably the most important and the hardest bit, especially without a disruptive event to drive it. Successful traditional organizations are in control at almost all times by a command-control way of organizing. Disadvantage is that this often requires a tremendous amount of alignment (i.e. conversation) between different silos to be able to make even small changes. A strong will to transform and the distribution of decision making authority to multi-disciplined agile digital teams will deliver digital change fast enough, good enough and cheap enough. 
    3. Use cloud technology unless. The only place where operations can be scaled fast without large CAPEX investments, access to all company data can be established, and innovative features are readily available. Probably more secure than homegrown solutions too.
    4. Mix own resources with external digital experts. When budgets are tight one cannot hire large external teams to do the job. This might be a blessing in disguise as digital requires a new way of working at organizations, which can only be sustained by its own employees. Gaps in expertise and methodology can be overcome by bringing in the right external digital experts and partners.      

Most organizations will have to accelerate their digital transformation capabilities, and most organizations will have less means to achieve this. Bootstrapped digital transformation might be a way out. This demanding fix combines a strong will to transform, distributed leadership, digital & AI technology, and the right team and right approach.


Podcast on AI for business

How about killing two birds with one stone?
Sit back, relax, listen to an episode of leadership insights by Future Processing and gain valuable insights on AI for business
Michal Grela interviewed our Robin Zondag. They covered:
➡️ Why would you bother doing AI in the first place?
➡️ How to approach an AI project?
➡️ What skills should your team possess?
➡️ How to align business, IT and Data?
You can find the links here ⬇️

The Benefits of failure

Fail fast, learn from your failures – encouraging people to take risks, move and learn fast is an essential part of any digital culture. It can be hard to do too. Therefore, some inspiration from J.K. Rowling in her classical ‘Benefits of Failure’ speech..

Chatbots serve customers better

Chatbots serve your customers better

In these customer centric times what better than a way to improve your customer service. In these P&L heydays what better than a way to reduce your cost. A chatbot is a great way to do both. Increasing customer satisfaction while at the same time reducing the cost of serving your customers. Please note this applies as much to ‘real’ external customers as to internal customers. Too good to be true? Let’s examine the technological developments, some real life experiences, perspectives on how to implement chatbots, and their bright future potential.

From zero to hero

People above 30 years old will remember Clippy the office assistant Microsoft included in Office for Windows from 1997 till 2003. Any user would try to silence Clippy as soon as possible as it was of absolutely no value. It would never come up with an answer for the problems one was experiencing. 

The days of Clippy are over and since a few years chatbots are becoming a very useful, always available and highly scalable tool to support users in getting what they need quickly and instantaneously.  

The biggest progress comes from AI and its ability to understand natural language. The user no longer has to describe his or her problem in exactly the same wording as the tool makers were expecting. AI is able to interpret the natural language pretty accurately and identify the intent of the user. Once the question is properly classified (“where can I find the print button….” e.g.) the chatbot feeds back the right answer from a knowledge base or executes an action (“buy this product…”). 

The world’s organizations are discovering the benefits of chatbots fast. Research& reports ‘the chatbot market size is projected to grow from USD 2.6 billion in 2019 to USD 9.4 billion by 2024, at a CAGR of 29.7% during the forecast period’. Gartner says Artificial Intelligence (AI) will be a mainstream customer experience investment in the next couple of years. 47% of organizations will use chatbots for customer care and 40% will deploy virtual assistants.

Implementing chatbots

There is a lot of magic talk about self learning chatbots. Don’t be fooled, with today’s technology creating and improving a chatbot still requires a significant amount of manual work:

  • Defining how and where to use the chatbot in the customer journey
  • Defining the intents correctly
  • Designing and implementing the tone of voice of the chatbot
  • Creating or collecting the knowledge articles
  • Keeping the knowledge articles up-to-date
  • Integration the chatbot with backend systems to execute tasks (e.g. “reset my password”) 
  • Analyzing interactions and improving the answers of the chatbot

Although getting the chatbot conversations right is definitely possible today, it is not a given. Forrester predicts that even in 2020 four out of five chatbot-based customer interactions will continue to flunk the Turing Test. Spiceworks provides an overview of the most common errors. 

A solid implementation methodology, thorough testing, reasonable expectations, picking the right conversation cases, and an experienced partner are amongst the key success factors for implementing meaningful chatbot conversations. 

Advancing technology will make things easier. For example, improving chatbot conversations is increasingly supported by strong analytical tools and automatic improvement suggestions. Also, technology is now enabling the automatic creation of new intents by scraping websites or uploading (somewhat structured) pdfs. This is e.g. part of Google Cloud’s Contact Center AI product. More and more chatbot vendors are including predefined workflows and connectors to make it easier to integrate with backend systems for the execution of identified tasks. These kinds of features reduce chatbot implementation and maintenance effort.

The next frontier, Voice is becoming mainstream

Biggest advancement however is today’s fast increasing maturity of voice technology. Where traditional chatbots require users to type to interact with them, the new wave of chatbots (or perhaps better, Voicebots) are able to interact by voice. Most mobile phones already have an assistant like Siri or Google Assistant installed, and more and more households have Alexa or Google Home (25% of US households in 2019). While American English still performs best it is quickly followed by mainstream European and Chinese languages. 

Humans are very comfortable and skilled in using voice to interact with each other. Now computers can do this too. Today many organizations have implemented e-commerce, webcare and chatbots to make customer interaction easier but still operate large call centers with agents talking to customers. This is expensive for organizations and often little appreciated by customers (wait time and frequent hand-overs). Voice technology promises to speed things up for customers as they can be served immediately anytime of day no matter the call volumes, to lower costs for organizations and to augment quality of work for agents as voicebots are likely to deal with routine tasks first. So get it right, and the future is bright!