At Philips, data is in the centre of everything we do. We believe that, every data item has a story of its own. If you have a penchant for unravelling the hidden patterns, then you are the person we are looking for!
To build our next generation smarts, you will be working with experts in a variety of fields, including clinical & regulatory specialists, UX designers, dental and skin care professionals, system architects, product management to integrate new technologies and refine system performance.
You will help build learning systems, leveraging data sets consisting of user interactions and actions, collected from hundreds of thousands of consumer devices, per day to model, analyse, and predict user behaviours.
While some of our algorithms run on resource constrained devices, others require large clusters on our cloud infrastructure.
As data scientist you will investigate, discern, and interpret data sets to recognize user behaviour and predict trends.
Job Qualifications :
Master’s degree or PhD in Computer Science, Information management, Statistics or related field, 7 - 12 years of experience in the Consumer or Healthcare industry manipulating data sets and building predictive models with focus on product development
Experience in statistical modelling, machine learning, data mining, unstructured data analytics and natural language processing.
Sound understanding of - Bayesian Modelling, Classification Models, Cluster Analysis, Neural Network, Nonparametric Methods, Multivariate Statistics, etc.
Strong hands on knowledge of ML techniques like regression algorithms, K-NN, Naïve Bayes, SVM and ensemble techniques like Random forest, AdaBoost etc
Having strong knowledge in unsupervised learning algorithms using Neural networks and Deep-Learning
Strong knowledge in Data Wrangling and Exploration techniques to identify the patterns, trends and outliners.
Deep knowledge and practical experience with data science toolkits, such as NumPy, Pandas, scikit-learn or equivalent
Experience with data visualization tools, such as QlikView, Matplotlib, seaborn or equivalent tools.
Proficiency in using query languages, such as SQL, PL / SQL
Hands on experience in the one or more databases like Hadoop, AWS Redshift, Snowflake etc.
Good applied statistics skills, such as distributions, statistical testing, regression, etc.
Good ETL scripting and programming skills, such as Python, R or Scala to integrate developed solution into the proposition.
A team player capable of working and integrating across cross-functional team for implementing project requirements. Experience in technical requirements gathering and documentation.
Ability to work effectively and independently in a fast-paced global collaborative agile team environment with tight deadlines
A flexible, pragmatic and collaborative team player with innate ability to engage with stakeholders at all levels in the organization.
A self-starter with high levels of drive, energy, resilience and a desire for professional excellence with a passion for data and data science