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When it comes to healthcare, global management consulting and technology firm ZS is a leading player. Headquartered in Illinois, the company has worked with top 50 pharmaceutical companies, and catered to 21 out of 25 top medical technology firms.
ZS covers the entire value chain of AI and ML, starting from research and extending to value realisation. It has an AI research lab dedicated to developing innovative and cutting-edge AI concepts. Their team of experts focuses on creating and deploying tech solutions that enable clients to expand their influence across different units, brands, and regions.
It has made an array of acquisitions in the health sector with the recent most being Trials.ai, a healthcare-focused, pure-play AI and analytics company. ZS also acquired analytics company Intomics that works with biomedical big data to infer biological insights and solutions providing digital health firm Medullan. Some of the primary clients include Pfizer, Eli Lilly and Company, Johnson & Johnson, Merck and so on.
“We are constantly experimenting with generative AI and AR/VR to create immersive experiences for our customers,” said Manish Menon, office managing principal, ZS, in an exclusive interaction with AIM.
ZS recruits associates, associate consultants and consultants.
Inside ZS’ AI & Analytics Team
With over 3,600 employees on the AI and analytics team, the team leverages cutting-edge tech to work on both client-billable projects and internal assets. The data science team is composed of professionals with different levels of expertise. There are currently 125 associates, 56 associate consultants, 31 consultants, 19 managers, and four associate principals in the team.
The company provides advanced AI-based data management solutions, specialising in semantic graph technology. Their smart algorithms optimise data services and enable in-house capabilities. They offer services like data lake development, warehouse migration, training, and organisational redesign. They also provide a customised solution integrating primary, secondary, and social data through their AI-powered cloud-native platform, ZAIDYN.
The team uses ML, statistical analysis and computing, deep learning, data visualisation, NLP, problem-solving skills, model deployment, data structures and algorithms, and neural networks in their daily work.
Additionally, the team also uses the following tools, applications and frameworks: Power BI, Microsoft Excel, Tableau, Python or R, image recognition, fraud detection, speech recognition, augmented reality, random forest, NumPy, XGBoost, Pandas and more.
Interview Process
“We look for candidates who understand data science, AI, ML, NLP, BERT, NER, supply chain, operational research, clinical trials, pharmaceuticals, healthcare, and have expertise in Python and R,” said Menon.
For junior-level positions, there are three interview stages that include coding challenges, aptitude questions, and ML case studies, panel presentation, 60-minute discussion of a real-world business problem and more.
On the other hand, consultants in senior positions have similar but more intrusive technical rounds where they discuss the candidate’s CV and assess their expertise in ML, advanced algorithms, and data science projects. In the unstructured case study round, candidates analyse a real-world business problem and develop a comprehensive framework for solving it. The evidence-based interview requires candidates to present a data science project, showcasing their approach, methodology, and valuable insights.
Read more: Data Science Hiring Process at HARMAN
Skills Needed
When it comes to the understanding of tech tools, data science candidates should be familiar with Power BI, Microsoft Excel, Tableau, Spark, Python, R, SAS, NLTK, among others. They should have sufficient knowledge in healthcare, image recognition, fraud detection, speech recognition, augmented reality, and a wide range of other domains. Moreover, candidates should also have a solid grasp of frameworks such as TensorFlow, Scikit-learn, Pandas, PyTorch, Matplotlib, NumPy, Seaborn, XGBoost, and random forest.
ZS uses several key performance areas (KPA) and key result areas (KRA) to assess the data science candidates.
KRAs
- Formulating questions aligned with the objectives of the organisation
- Performing data inquiries and exploratory analyses to address those inquiries
- Consolidating and manipulating data from diverse origins
- Selecting suitable models and algorithms to steer the data analysis procedure
- Proficiency in coding languages such as Python and R
KPAs
- Net promoter score (NPS): This metric captures the level of customer satisfaction gathered through surveys and distributed among internal stakeholders.
- Dollars saved (or earned) through data products: Evaluates the value generated by the team for the company in terms of monetary savings or gains.
- Number of incidents per product monitored: Checks the dependability of data science products by measuring the frequency of incidents per product.
- Time to incident resolution: Examines the efficiency of data science teams in addressing and resolving incidents, ensuring that key products are restored promptly.
- Cloud computing costs per team member: Quantifies the expenditure on cloud computing resources per team member, offering insights into the efficient use of computational resources by data science team members.
Expectations
Menon emphasised on the fact that the candidate looking to pursue a career in data science should make it a standard practice to gather information from the hiring team about the company, the clients they will work with and the specific skill set they will be hired for.
It is important to have an appealing resume that showcases your previous projects and proficiency in different data science skills. Employers also prefer candidates who have a consistent work history and have good communication skills and practical problem-solving approach. “Candidates should be transparent and honest about their preferences and counteroffers to foster a better working relationship with the hiring team and provide a foundation for successful collaboration,” Menon explained.
Read more: Data Science Hiring Process at Pepperfry
Work Culture
ZS is a values-driven organisation that aims to foster collaboration and teamwork towards shared goals based on principles of impact, growth, and empowerment.
ZS India boasts a 31% female representation in its workforce and has adopted a merit-based approach in hiring and promotions with clear policies around inclusivity, as well as a zero-tolerance policy for any violations of these values. “We aim to create a supportive and dynamic work environment that promotes personal and professional growth and cultivates a shared sense of purpose among our employees,” Menon added.
In addition to providing employee rewards and benefits like health insurance, maternity leaves, food, and transportation allowances, ZS also provides the following perks.
- Paternity, adoption and family medical leaves
- Mental health support for employees and family members, financial planning services and legal counselling
- Wellness programs—from nutritional and physical to stress management
- Employee retention bonus with increasing amounts over four years
So, if you are looking to make an impact as a data scientist in an environment that helps you grow both professionally and personally, ZS is the right fit for you.
