Danaher Corporation Manager, Data & ML Engineering in Bangalore, India
Danaher Digital is our digital innovation, incubation and acceleration center where we’re bringing together the leading strategic product and business leaders, technologists and data scientists for the common purpose of accelerating development and commercialization of disruptive and transformative digital solutions into the marketplace.
We accelerate Danaher’s digital innovation journey by partnering with Danaher operating companies to develop and commercialize emerging and disruptive digital technologies such as AI, Machine Learning (ML), Big Data, IoT, Augmented Reality (AR), Cloud (SaaS/PaaS) and other Digital frontiers. If you are driven to forge new disruptive and transformative digital apps, platforms and services by working with such cool and emerging technologies, you belong in Danaher Digital.
As a member of Danaher Digital, you will help identify new product ideas and operating models, and then design, develop and deliver them. Working together with our operating companies, you will also help foster and support an entrepreneurial culture that will push Danaher to launch new software and data products better, and faster.
To learn more about Danaher Digital and our team, please visit www.danaherdigital.com or visit www.linkedin.com/company/danaher-digital/about.
As a Manager for Data and Machine Learning(ML) Engineering, you will join a team of skilled Data Scientists, Software engineers and Cloud Architects to drive Danaher’s Digital transformative initiatives in Data and Analytics (Machine Learning/AI) platforms and applications targeted at multiple industrial segments such as Life Sciences, Diagnostics, Industrial manufacturing and environmental sciences. You will lead a team of engineers tasked with building cloud-based data and analytics services to translate Danaher’s strategic vision in to technical reality.
You will be called upon to work collaboratively with our business stakeholders, Architects, Product Managers/Owners, to set goals for your team and guide them with hands-on technical expertise. You will not hesitate to get your hands dirty in technical implementations. You will bring your proficiency in Data Engineering, SQL/NoSQL databases, ML Engineering, analytics pipelines, SQL/NoSQL databases and detailed SW planning/execution acumen.
You will have the opportunity to build new teams and mentor them to become highly efficient in what they do and in on-time delivery of tasks. You will work with a globally distributed Agile team in a fast-paced environment.
Lead a team of skilled engineers to build data pipelines and production level ML infrastructure in a fast-paced environment.
Lead and manage your team of Data and ML engineers to translate Data & Analytics requirements in to short- and long-term implementation plans. Be comfortable with details and be hands-on to make sure the delivery expectations are met.
Lead your team to launch new data ingestion, extraction, transformation and loading processes on AWS/Azure cloud with a keen focus on scalability, reliability, performance and reusability. Build key data sets and lead feature engineering efforts to empower exploratory analysis and advanced analytics.
Collaborate with our data scientists to identify and build data pipelines and patterns that are relevant to advanced analytical model building and then curate, clean, wrangle and prepare data for efficient use at large scale.
Lead your team to understand Machine Learning/Deep Learning model performance requirements, refactor model code as necessary, design model deployment frameworks and deploy models in prototype/production environments.
Closely collaborate with other engineering teams to ship machine learning products to production.
Interact with both business and technical stakeholders to deliver high quality products and services that meets/exceeds business customer, and technical requirements.
Leverage your experience to evaluate new data technologies and build a scalable data engineering and ML engineering framework.
Share in code and design reviews with agile team
Integrate 3rd party software components into existing software applications
Work with geographically distributed teams while maintaining highest standards in collaboration and communication.
10+ year of demonstrated experience in developing highly scalable, reliable, and real-time data processing pipelines combined with experience in Machine Learning workflow and model deployment
7+ years of experience leading and software product development teams in an Agile environment
7+ years of experience with a variety of SQL and No-SQL data stores such as MongoDB, Cassandra, HBase, MySQL/Postgres
5+ years of demonstrated experience in developing data pipelines using Python/Java/Scala on various frameworks(especially on Apache Spark) on AWS, Azure, or similar cloud platforms; Demonstrated experience in Data security aspects and implementation.
3+ years of demonstrated experience in designing and deploying software using frameworks for machine learning such as TensorFlow, Theano, Keras, Scikit-learn, Spark ML, CNTK, Matlab, Torch, Caffe, MXNet, H2O
Ability to work with structured, semi-structured and unstructured datasets uncovering information and identifying complex links across different data sets
Experience with Docker and Kubernetes
Experience with one or more programming languages such as Java, Scala or Python.
Ability to nurture/mentor others in the team.
A can-do attitude in anticipating and resolving problems to help your team to achieve its goals.
Excellent communication skills with direct team members as well as external teams and stakeholders.
Must have experience in Agile development methods.
Willingness to travel (<10%)
Danaher Corporation and all Danaher Companies are equal opportunity employers that evaluate qualified applicants without regard to race, color, national origin, religion, sex, age, marital status, disability, veteran status, sexual orientation, gender identity, or other characteristics protected by law. The “EEO is the Law” poster is available here (http://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf) .