Data Scientist, Signal Processing

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

Verily, an Alphabet company, lives at the intersection of technology, data science and healthcare. Our mission is to make the world’s health data useful so that people enjoy longer and healthier lives.

Our team combines expertise in healthcare, data science and technology to improve the health and well-being of our communities. We are developing the infrastructure and solutions to harness the profusion of health information for good. Our data-driven solutions span three primary areas: research, care and innovation. Programs include Project Baseline - our research initiative to increase participation and evidence generation in clinical research; Onduo - our personalized virtual care platform, which includes connected tools, lifestyle coaching and clinical support; and Debug - our effort to reduce the threat of mosquito-borne diseases by combining machine learning with sterile insect technique. We’re also actively working to combat the spread of COVID-19 through new programs like Healthy at Work.

DESCRIPTION

As a Data Scientist for Verily, you will work cross-functionally with Verily's clinical, software and science teams to create algorithms and methods for processing existing and novel bio-sensor data. These efforts combine wearable sensing with traditional clinical tests and health outcomes in an effort to change the way various diseases are understood and treated. You will leverage specialized sensors and robust cloud infrastructure to capture and analyze physiological data. You will apply your skills to develop signal processing and analytic methods applicable to large, diverse and complex data sets, often derived from noncontrolled real-world settings. You will deliver key analyses for motivating product direction, build reusable analysis tools and work closely with software engineers, product managers and clinicians to deliver user-facing products.

RESPONSIBILITIES

• Work with large, complex data sets in the sleep domain. Solve difficult, non-routine analysis problems, handling data challenges from a real-world setting.
• Develop quality control and pre-processing tools for a broad range of digital data types.
• Iterate on and build new models from wearable sensors using a combination of physical modeling, signal processing and machine learning approaches.
• Apply advanced analytical methods that process continuously measured physiological and cardiac features in a real-world population.
• Clearly communicate highly technical results and methods in well structured presentations/reports, as well as interact cross-functionally with a wide variety of people and teams.

QUALIFICATIONS

Minimum qualifications:

• Masters degree in Computer Science or related technical field, or equivalent practical experience.
• 3+ Years experience with:
  
• Python or similar.
• Exploratory and statistical data analysis (such as linear models, multivariate analysis, predictive modeling and stochastic models). Experience extracting and cleaning data sets.
• Physiological signal processing and analysis of time-series data.
• Machine learning (supervised and unsupervised methods).

Preferred qualifications:

• 2+ years of relevant work experience (i.e., as a biomedical engineer, data scientist, computational biologist), including deep expertise and experience with cardiac signal processing.
• Experience with the analysis of wearable sensors and clinical data.
• Solid Data Science Python experience (NumPy, SciPy, Pandas, Scikit-Learn, Matplotlib etc.).
• Familiarity with software engineering practices and experience developing production software.
• Demonstrated willingness to both teach others and learn new techniques.
• Familiarity with Deep Learning frameworks (TensorFlow, PyTorch etc.).
• PhD degree in a quantitative discipline (e.g., biomedical engineering, computer science, applied mathematics or similar) or equivalent practical experience.