Judith Kniepeiss - Recruiter Profile | Harnham Recruitment

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Judith Kniepeiss

Managing Consultant - Germany

Bio

After earning her master’s degree in Strategy, Organisation and Leadership in Denmark, Judith joined the European Team at Harnham. As an Austrian native she is responsible for the Digital Analytics practice in the DACH region. In the past months, Judith has helped to establish Harnham as the leader in Data and Analytics recruitment in the German speaking region. On a daily basis Judith supports clients from different industries to find the right talent for their teams.

Harnham blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out our recent posts below.

Life Science Analytics Lead the Way to a New Normal

The Life Science Analytics industry has always beaten to its own drum. But in the days of Covid-19, there’s a different feel and it’s one in which teams are coming together and candidates are staying longer in jobs where they feel connected and impactful. As the drive for a vaccine and the virtually overnight demand for telemedicine and contactless care come to bear, this industry which once seemingly fell behind that of retail and banking has caught up. So, what can businesses like biotech, pharmaceutical, and other healthcare providers do to retain and keep top candidates? EXPAND AND GROW YOUR TEAM LOCALLY AND GLOBALLY Reskill and Upskill for Career Advancement - If you’re lucky enough to have retained top talent, re-consider tenure-based positions. Advance your great candidates based on performance, need, upskilling, or reskilling. You may already have someone on staff who can do the job you need done or have the potential. Let them. The world has been moving faster than it ever has in this year alone its jumped into warp speed.Consider Global Collaboration – While many professionals, in every industry are working from home these days, some simply can’t due to the nature of their business. In this case, the need to be in the lab. However, as the Life Sciences & Analytics industry leads the way in their approach to flexible hours and the available Data on COVID-19, for example, global collaborations allow teams to do their work without the need for lab access.In demand technical skills - Candidates skilled in Data gathering, algorithm development, and predictive modeling are in high demand as well as AutoML, NLP, and other Machine Learning solutions. In demand soft skills – As the impact of the above technical skills increase and offer proven solutions, it will be important to have Data professionals who cannot only manage the technical side of things, but who can also explain solutions to the nontechnical and business executives in plain language. Since the start of the year, we’ve seen a massive shift in the way we do business. While for some businesses, it was business as usual for the most part. For others, it completely reinvented others. Healthcare and Life Sciences are no exception. And in the healthcare industry, they’ve been stretched in ways unimaginable just last year. And have learned a new respect for numbers and accurate Data. Two things vital to moving forward. A NEW RESPECT FOR NUMBERS AND ACCURATE DATA This new respect for accurate numbers and Data will help teams align to predict new threats while tracking current ones. In other words, no one will be caught off guard next time as the Life Sciences and Healthcare industry prepare for a post pandemic transformation. And how will it impact the industry moving forward? Work from home policies, global teams, telemedicine, the demand for PPE and ventilators, even the demands of the financial side of healthcare have shifted. But with the right data, innovation, and improved efficiency, it’s a sure bet the industry won’t be caught unawares again. WELCOME TO THE NEW NORMAL Though every profession has been hard hit during the pandemic, it’s the healthcare industry which has seen an even greater shift in the need demands to be met, shifting priorities, and patient care delivery has gone online. By moving forward with telemedicine and other automated services, the revenue cycle of the industry, too, has seen a shift. Yet to maintain business continuity, they must close the revenue gap. And here’s where Life Science Analytics meets FinTech and InsurTech. All of these industries will need Data professionals who can speak code and translate it to the nontechnical. All will need professionals with skillsets in predictive modelling, automation, Machine Learning, AI, and more. Is it you they’re looking for? If you’re interested in Data & Technology, Risk or Digital Analytics, Life Science Analytics, Marketing & Insight, or Data Science jobs we invite you to check out our current vacancies or get in touch with one of our expert consultants to find out more.

by Judith Kniepeiss

30. July 2020

Es gibt in jeder Industrie Frauen an der Spitze: Eine Fragerunde mit Rachel Stuve

Wir hatten kürzlich die Gelegenheit mit Rachel Stuve, einer von LinkedIns Top-Stimmen in Data Science & Analytics und Geschäftsführerin der Data Teams, zu sprechen. Als eine Expertin in ihrem Gebiet verfügt Stuve über ein breites Erfahrungsspektrum. Nachdem sie die Universität im automobil-lastigen Michigan absolviert hatte, war ihr erster Job bei Chrysler die Autoindustrie zu analysieren. Kurz nachdem sie in die lokale Verwaltung gewechselt war, digitalisierter und integrierte sie deren Strafverfolgungsprozesse, bevor sie an einer landesweiten Initiative zum Datenaustausch arbeitete. Zuletzt war das Gesundheitswesen Stuves Fokus. Während viele diese Branche als hochspezialisierte, unzugängliche Branche ansehen, ist Stuve einer anderen Meinung. "Es sind alles übertragbare Fähigkeiten. Möglicherweise betrachten Sie unterschiedliche Datensätze bei einem Gesundheitsdienstleister, aber im Wesentlichen, erfolgt die Analyse nach denselben Prinzipen. Dennoch stellt Stuve klar, dass man trotzdem einige Hürden, vor allem terminologische, überwinden muss. "Zugegeben, der Jargon ist etwas gewöhnungsbedürftig und es gibt davon viel." Jedoch die Hauptunterschiede sind weniger wissenschaftlicher Art und mehr in Hinblick auf die Infrastruktur. Anders als viele datengesteuerten Industrien liefern Krankenversicherer nicht direkt an den Verbraucher. Tatsächlich ist deren eigentliche Beziehung mit den Gesundheitsdienstleistern. "Es ist nicht dasselbe, wie eine Hypothek aufzunehmen. Sie werden sich nicht direkt an ihren Versicherer, damit dieser sich um Sie kümmert. Ihr direkter Service ist der Gesundheitsdienstleister, das Krankenhaus und es ist lediglich die Aufgabe des Versicherers die Zahlungen zu decken. Ein Teil der Herausforderung ist es, herauszufinden, welcher Anbieter das beste Preis-Leistungs-Verhältnis aufweist und welcher eine hochwertige Versorgung anbietet." Dies bedeutet, dass es wichtig ist, ein Team aus Datenwissenschaftlern und Epidemiologen zusammen zusetzten. Epidemiologien können besser identifizieren, welche Behandlungen erzielen den größten Erfolg, bei möglichst geringen Kosten. Wie kann man nun solch ein Team, mit unterschiedlichen Hintergründen und Herangehensweisen dazu bringen, harmonisch miteinander zu arbeiten? "Ob von Anfang an die richtigen Ziele vereinbart wurden, hat großen Einfluss auf den Erfolg eines Projekts. Wenn sich alle Teammitglieder einigen können, wie genau Erfolg aussieht, sei es 10, 20% Gewinnsteigerung, oder ein anderes Ziel, dann weiß jeder worauf man hinarbeitet. Natürlich gibt es ab und an Diskussionen über statistische Gespräche, aber schlussendlich ziehen alle an einem Strang." Stuve betont auch, wie wichtig es ist, die richtigen Personen in der richtigen Projektphase einzubeziehen. All zu oft werden die Endnutzer nicht in die frühen Datenprojektphasen miteinbezogen, was zu großen Wissenslücken führt. Stuve stellt fest: "Mit ziemlicher Sicherheit werden Sachen übersehen, wenn diejenigen, die wirklich wissen, was sie von einem Projekt benötige, nicht von Anfang an miteinbezogen werden." Neben ihrer Arbeit im Gesundheitswesen, investiert Stuve ihre Arbeit bei Golden Seeds auch in von Frauen geführte Start-ups. Eine Angelegenheit, die ihr besonders am Herzen liegt. "Ich liebe Golden Seeds. Es existieren zahlreiche Studien, die belegen, dass Frauen geführte Unternehmen höhere Rendite erzielen, dennoch erhalten diese Unternehmen, im Vergleich zu von Männern geführte Unternehmen, nur einen Bruchteil von Investitionen." Sie verweist auf einen kürzlich in der Harvard Business Review veröffentlichten Artikel, weshalb dies sein könnte. Laut des Artikels besteht, bei dem Investmentprozess, eine gewisse Geschlechtervoreingenommenheit. Männlichen Unternehmer werden nach dem Potenzial des Unternehmens gefragt, weibliche Unternehmerinnen hingehen eher nur rein risikomindernde Fragen. "Leute investieren in Optimismus. Wenn Unternehmerinnen also nicht die gleiche Möglichkeit gegeben werden den 'Traum' zu verkaufen, dann ist die Wahrscheinlichkeit eine Investition zu bekommen um einiges geringer." Auch glaubt Stuve, dass eine Wahrnehmung besteht, dass von Frauen geführte Unternehmen weniger innovative sind: „Ich möchte das Verständnis ändern, dass diese Unternehmen als „mädchenhaft“ bezeichnet werden und sich ausschließlich auf Kleidung, Lebensmittel oder den Einzelhandel konzentrieren. Das ist, meiner Erfahrung nach, nicht der Fall. Frauen stehen an der Spitze aller Branchen, von Biotechnologie über Energie bis hin zu einer Reihe von Fachgebieten.“ Also, worauf achtet sie beim Investieren? "Natürlich suche ich nach einer innovativen Idee, welche ein Geschäftsbedürfnis erfüllt, aber zugleich investiere ich auch in die Person. Sind sie realistisch? Sind sie starke Anführer? Sind sie sich ihren eigenen Schwächen bewusst und haben sie ein Team um sich herum aufgebaut, welches diese Schwächen ausbalancieren kann?" "Leider besteht eine Doppelmoral, wenn es um die Wahrnehmung männlicher und weiblicher Führungskräfte geht. Dies bedeutet, dass die Art und Weise, wie sie sich selbst tragen, einen großen Unterschied macht, besonders wenn sie nach weiteren, zukünftigen Investitionen suchen." Stuve ist sich der Schwierigkeiten, mit denen Frauen in Männerdominierenden Brachen konfrontiert werden, durchaus bewusst. Sie selbst war in vielen Teams die einzige Frau, insbesondere als sie in das Management aufstieg. Glücklicherweise sieht sie ein Licht am Ende des Tunnels: "Unternehmen fangen an zu erkennen, wie wichtig es ist, die Vielfalt ihrer Teams zu erweitern und das Unternehmensgespräch passen sich dies auch an." "Darüber hinaus besteht ein großartiges Data-Netzwerk von Frauen. Das Verknüpfen hat einen Schneeballeffekt. Sie verknüpfen sich mit einer Person, welche ihnen eine andere Person vorstellt, die ihnen wieder eine andere Person vorstellt und so weiter, bis ihnen die Reichweite dieser erstaunlichen Gemeinschaft außergewöhnlicher Frauen bewusst wird." Wenn Sie mehr von Rachel Stuve hören möchten, können Sie ihr auf LinkedIn folgen, um regelmäßig mit Updates und Ideen versorgt zu werden. Für weitere Informationen zum aktuellen Stand der Vielfalt in Data & Analytics finden Sie hier in unserem Bericht zu diesem Thema. Wenn Sie Ihr Team ausbauchen möchten oder nach einer neuen Herausforderung suchen, können Sie sich an einen unserer Fachberater wenden oder sich hier über unsere neuesten Möglichkeiten informieren.

by Judith Kniepeiss

21. April 2020

3 Ways Machine Learning Is Benefiting Your Healthcare

With Data-led roles leading the list in the World Economic Forum’s ‘Jobs of the Future’ report, it is no surprise that Data Science continues to be the main driving force behind a number of technological advancements. From the Natural Language Processing (NLP) that powers your Google Assistant, to Computer Vision identifying scanning pictures for specific objects and the Deep Learning techniques exploring the capability of computers to become “human”, innovation is everywhere. It’s unsurprising, then, that the world of healthcare is fascinated by the possibilities Data Science can offer, possibilities which could not only make your and my life better, but also save several thousands of lives around the world. To just scrape the surface, here are three examples of how Machine Learning (ML) techniques are being used to benefit our healthcare. COMPUTER VISION FOR IMAGING DIAGNOSTICS Have you ever had a broken leg or arm and saw a x-ray scan of your fracture? Can you remember how the doctor described the kind of fracture to you and explained where exactly you can see it in the picture? The same thing that your doctor did a few years ago, can now be done by an algorithm that will identify the type of fracture, and provide insights into how you should treat it. And it’s not just fractures; Google's AI DeepMind can spot breast cancer as well as your radiologist. By feeding a Machine Learning model the mammograms of 76,000 British women, Google’s engineers taught the system to spot breast cancer in a screen scan. The result? A system as accurate as any radiologist. We‘ve already reached the point where Machine Learning and AI can no longer just outsmart us at a board game, but can benefit our everyday lives, including in as sensitive use-cases as the healthcare industry. NLP AS YOUR PERSONAL HEALTH ASSISTANT When we go to our GP, we go to see someone with a medical education and clinical understanding who can evaluate our health problems. We go there because we trust in the education of this person and their ability to give us the best information possible. However, thanks to the rise of the internet, we’ve turned to search engines and WebMD to self-diagnose online, often reading blogs and forums that will convince us we have cancer instead of a common cold. Fortunately, technology has advanced to the point where it can assist with an on-the-spot (much more accurate) evaluation of your medical condition. By conversing with an AI, like the one from Babylon Health, we can gain insights into possible health problem, define the next steps we need to take and know whether or not we need to see a doctor in person. There’s no need to wait for opening times or to sit bored in a waiting room. Easy access from your phone democratises the process and advice can be received by anyone, at any time. DEEP LEARNING DRAWS CONCLUSIONS BETWEEN MEDICAL STUDIES Despite their extensive qualifications, even medical researchers can feel overwhelmed by the sheer amount of Insights and Data that are gathered around the world in hospitals, labs, and across various studies. No wonder it’s not uncommon for important Insights and Data to get forgotten in the mix. Once again, Machine Learning can help us out. Instead of getting lost in a sea of medical data, ML algorithms can dig deep and find the information medical researchers really need. By efficiently sifting a through vast amounts of medical data, combining certain datasets and providing insights, ML sources ways for treatments to be improved, medicines to be altered, and, as a result, can save lives. And this is only the beginning. As Machine Learning continues to improve we can expect huge advances in the following years, from robotic surgery to automated hospitals and beyond. If you’re an expert in Machine Learning, we may have a job for you. Take a look at our latest opportunities of get in touch with one of our expert consultants to find out more.

by Judith Kniepeiss

13. February 2020

How NLP Is Redefining The Future Of Tech

During the last half of the past decade the importance of Data reached a level at which it was coined “the new oil”. This was indicative of a shift in the practices of individuals and businesses, highlighting how they now rely on something which isn’t measurable in gallons but in bytes. However, because we can’t physically see the Data we generate, gather and store, its easy to lose our connection to it. This is where NLP is comes into play. With the purpose of helping computers understand our languages, NLP (Natural Language Processing) gained an increased importance over the last couple of years. But, more than teaching a computer how to speak, NLP can make sense of patterns within a text, from finding the stylistic devices of a piece of literature, to understanding the sentiment behind it. So, with NLP set to become even more prevalent over the next decade, here are some of the ways in which it’s already being put to use: EXTRACTION Like an advanced version of using Ctrl + F to search a document, NLP can instantly skim through texts and extract the most important information. Not only that, but NLP algorithms are able to find connections between text passages and can generate statistics related to them. Which leads me to my next example: TEXT CLASSIFICATION This is fairly self-explanatory: NLP algorithms can parameters to categorise texts into certain categories. You’ll find this used frequently in the insurance industry, where businesses use NLP to organise their contracts and categorise them the same way newspapers categorise their articles into different subcategories. And, closer to home, it’s similar algorithms that keep your inbox free from spam, automatically detecting patterns which are heavily used by spammers. But NLP does more than just look for key words, it can understand the meaning behind them: SENTIMENT ANALYSIS Sentiment Analysis takes the above understanding and classification and applies a knowledge of subtext, particularly when it comes to getting an indication of customer satisfaction. For example, Deutsche Bahn are using Sentiment Analysis to find out why people are unhappy with their experience whilst Amazon are using it to keep tabs on the customer service levels of their sellers. Indeed, Facebook have taken this one step further and, rather than just tracking satisfaction levels, they are examining how users are organising hate groups and using the data collected to try and prevent them mobilising. With the advancement of Machine Learning and technological developments like quantum computing, this decade could see NLP’s understanding reach a whole level, becoming omnipresent and even more immersed in our daily lives: PERSONAL AI ASSISTANTS The popularity of using personal AI-based assistants is growing thanks to Alexa and Google Assistant (Siri & Cortana not so much, sorry). People are getting used to talking to their phones and smart devices in order to set alarms, create reminders or even book haircuts. And, as we continue to use these personal assistants more and more, we’ll need them to understand us better and more accurately. After decades of using generic text- or click inputs to make a computer execute our commands, this decade our interactions with computers need to involve into a more “natural” way of communicating. But these advances are not just limited to voice technologies. Talking and texting with machines, the way we would with friends, is increasingly realistic thanks to advances in NLP: CHATBOTS Since companies have realised that they can answer most generic inquiries using an algorithm, the use of chatbots has increased tenfold. Not only do these save on the need to employee customer service staff, but many are now so realistic and conversational that many customers do not realise that they are engaging with an algorithm. Plus, the ability to understand what is meant, even when it is not said in as many words, means that NLP can offer a service that is akin to what any individual can. If you’re interested in using NLP to fuel the next generation of technical advancements, we may have a role for you. Take a look at our latest opportunities or get in touch with one of expert consultants to find out more.

by Judith Kniepeiss

23. January 2020

MeasureCamp Berlin: A Preview

In preparation for this year's MeasureCamp Berlin, we sat down with Benjamin Bock, communications lead, to discuss what to expect, as well as his thoughts on the industry in general. Here's what he had to say: Can you explain MeasureCamp for people who haven’t been yet? MeasureCamp is an open, free-to-attend analytics 'un-conference' made by analytics professionals for analytics professionals (and everyone who wants to get there) around the globe. In that sense, it’s different to any conference you know of. Our schedule is created on the day of the event, and our speakers are fellow attendees. Listen to talks, give a talk, and discuss topics that really tickle your fancy. What can we expect at MeasureCamp Berlin this year? Let’s begin with what you can’t and never will expect at MeasureCamp Berlin: Sales pitch presentations. We’ve all been there… you are visiting a fancy, expensive conference and all you get is Heads of 'This n’ That' talking about what their team did, what they spent money on and that you should buy Product X to be as Data-driven as them (mind the cynicism). At MeasureCamp you can expect talks and discussion rounds by around 150 fellow experts, who all know the daily adventures of cleaning Data, setting up analytics or debugging tracking code or running mind-bending analysis first hand. What is your best tip for someone that has never been at MeasureCamp before? Don’t rush it! MeasureCamp is about mingling with the analytics community as much as it is about the talks and discussion rounds. Pick a few talks that really interest you and use the rest of the day to get to know other attendees. Our awesome sponsors are also more than happy to talk to you. What is the best advice you got last year at MeasureCamp? On a personal level, I was able to get some really good advice when it came to data privacy topics. GDPR was still fairly fresh and nobody really knew if what they had done was actually enough to not get into trouble. That’s the kind of advice you only get if you have the chance to talk to other professionals face to face. On another note, what are the most sought-after skills and technologies currently used? I can only speak of my experience here. On a hard skill level and depending on the individual role, you need a solid understanding of web technologies (JavaScript, HTML, CSS) and tag managing systems to be able to implement tracking (plus some knowledge in mobile development when your focus lies on apps). When it comes to analysing and visualising Data, you should understand the tool you are working with and its underlying Data-structures. Being able to retrieve tool-agnostic Data with SQL and running more sophisticated calculations (e.g. with Python) has become more and more important over the last few years. But there are some softer skills, that should not be overlooked as well. As an analytics professional, you should never assume that your knowledge and language are common ground. You need to be a strong communicator, who is able to explain complicated concepts broken down to the absolute basics. In your opinion, what will be the biggest challenge in digital analytics in the next year? Two weeks ago, I would have answered “bringing web and app Data together”. Now that we know Google is working on that topic, it’s still a challenge, but one I am happy to tackle in the coming year. Digital Analytics is constantly changing. What do you expect to be the most talked about topic at MeasureCamp this year? As a Tracking Specialist with a focus on Google products, I’d love to hear some talks about Google Tag Manager Custom Templates. But my top guess is, that the newly released Apps and Web properties beta for Google Analytics will be the talk of the hour. MeasureCamp Berlin is an open and free-to-attend 'un-conference', taking place this year on the 28th of September. The final batch of tickets will be released on the 21st of August at 03:00 PM (CEST). Click here for more information and to get hold of your place.

by Judith Kniepeiss

20. August 2019

Erfolgreicher Aufbau einer Data Science Kompetenz

Ich habe gerade ein interessantes Interview mit dem Geschäftsführer der Fraunhofer-Allianz Big Data gelesen und stimme ihm zu, dass ein erfolgreiches Data Science Team unterschiedliche Kompetenzen bündeln sollte.Ich bin mit vielen Unternehmen in Kontakt, die am Aufbau und Ausbau ihrer Data Science Teams arbeiten. Ein entscheidender Erfolgsfaktor für die Zusammenstellung eines neuen Teams sind realistische Erwartungen.Der Trugschluss - ich stelle einen einzigen Data Scientist ein und somit sind alle meine Data Themen gelöst – führt zu mehr Enttäuschung als Mehrwert, sowohl bei dem Kandidaten als auch bei dem Unternehmen. Um das volle Potenzial eines Data Scientists ausschöpfen zu können, ist ein Team mit unterschiedlichen Kompetenzen sowie die Unterstützung durch Data Engineers und Data Architekts von hoher Bedeutung.

by Judith Kniepeiss

07. March 2017

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