Job Details
Skills And Tools:
Job Description
Do you want to build your brand by working for a leading firm that drives eminence in the marketplace? Are you interested in leveraging your analytical skills and strategic ideas to improve mission execution? If so, Garonz could be the place for you! Garonz is a multinational company that bridges the gap between AI technology and business applications in industries we believe push the world forward. We are looking for an entrepreneurial Machine Learning Engineer with natural language processing expertise. This MLE will work closely with the team to quickly execute and iterate on the product on its path to product market fit. We are an Agile team building flexible and scalable systems that can adapt quickly to leverage new data sources or changes in channel and target.
Want to do more than just imagine the ways our world will move tomorrow? Join the technology company that’s transforming the future of mobility today.
Position
Hands-on programmer with active development skills in a production machine learning or data-science environment. Apply practical skills to create hybrid machine learning and rule-based models using our innovative tools for solving real-world problems. Collaborate with data-science, engineering and customers to build production machine learning models and provisioning systems.
We're building a new Machine Learning/Data Science team at Garonz. This team is positioned for quick impact, visibility, and growth as embedding Machine Learning solutions across the business is a top priority for leadership.
Join us, and tackle some of the most challenging problems in natural language processing and large scale applied machine learning. You will build cutting edge natural language understanding technologies and deploy them on a global scale.
Responsibilities
- Develop scalable tools leveraging machine learning and deep learning models to solve real-world problems in areas such as Speech Recognition and Natural Language Processing.
- Lead your own project. Suggest, collect and synthesize requirements. Create an effective roadmap towards the deployment of a production-level machine learning application.
- Develop data pipelines that unify various data sources into one cohesive platform for data access.
- Evaluate different NLP technology and tools.
- Design and Develop NLP engine and applications.
- Develop sourcing, cleansing, structuring and ingesting new data sources.
- Apply machine learning and predictive modelling techniques.
- Develop creative solutions to business problems using mathematical algorithms.
- Gather and process raw data at scale (including writing scripts, web scraping, calling APIs, write SQL queries, writing applications, etc.).
Job Requirements
Requirements
- MS or PhD in a quantitative discipline, e.g. Computer Science, Machine Learning, Mathematics, Operations Research, Data Science. If BS only, 2-5 years of hands-on experience developing machine learning models.
- Proficiency in algorithms, data structures and computer science fundamentals.
- Hands-on experience with machine learning platforms and related tools.
- Deep knowledge of statistics and probability theory.
- Solid background in machine learning concepts and probability theory
- Experience with Machine Learning techniques for supervised & unsupervised learning e.g. CNN's, DNN's & RNN's.
- Strong knowledge of Machine Learning frameworks such as MXNet, Keras, TensorFlow, Theano, etc.
- Experience with Natural Language Processing.
- Excellent programming skills preferably with Python.
- Strong working knowledge of Databases and SQL.
- Expertise in machine learning techniques such as neural nets, embeddings and representation learning, predictive modeling, clustering, recommendation systems, bandit methods, reinforcement learning, anomaly detection, tree-based methods
- Expertise in statistical testing and experimental design
- Familiarity with software engineering industry processes
- Excellent analytical and problem-solving skills
- Excellent written and verbal communication skills
Beneficial Skills
- Hands-on experience with rule-based models
- Working familiarity and understanding of tools and libraries such as: Weka, Stanford NLP, Apache UIMA, Apache Mahout, NLP, Tableau, Deep Learning, Tensor Flow, Pytorch, Caffe
- Solid time series analysis, speech recognition, NLP or financial engineering background.
- Strong background in Mathematics and Statistics.
- Experience with GPUs and cloud-based training of deep neural networks.
- Contribution to open-source projects on Machine Learning.
- Knowledge in Reinforcement Learning or Meta Learning.
- Experience with big-data technologies such as Hadoop, Spark, SparkML, etc.
Bonus Attributes
- Strong passion for creative content curation and building brand awareness
- Passion for business and enthusiastic about taking part in shaping Garonz’s growth
- Function well in a very fast-paced startup environment
- Track record of excelling in small teams
- Team players who thrive in uncertainty and like to “make things happen”!
What we commit to you
- Competitive remuneration package in a rapidly-expanding startup
- Work in a collaborative, co-creation hub in the heart of the city - with amazing facilities
- Comprehensive medical insurance coverage, including dental
- Generous leave policy, including a ‘work remote policy’
- The opportunity to travel and work around the globe with our international clients and growing number of offices (New York, Venezuela, Columbia)
- The opportunity to be a part of something impactful
- Strong work ethic: All of us believe in our work’s ability to change human lives. Consequently, we work not just smart, but also hard.
- Growth mindset: We are eager to teach you new skills and invest in your continual development. But learning is hard work, so this is something we hope you’ll want to do.
- Good team member: We care and watch out for each other. We’re humble individually and go after big goals together.
- Flexibility: You should be flexible in your tasks and do whatever is needed, ranging from lower-level tasks such as coordinating complicated schedules, to high-level work such as thinking through corporate strategy.
