Upwork ($UPWK) is the world’s work marketplace. We serve everyone from one-person startups to over 30% of the Fortune 100 with a powerful, trust-driven platform that enables companies and talent to work together in new ways that unlock their potential.
Last year, more than $3.3 billion of work was done through Upwork by skilled professionals who are gaining more control by finding work they are passionate about and innovating their careers.
Despite our success, we believe we can do better, which is why we’ve assembled a world-class team from around the world in fields ranging from computer science and electrical engineering to economics and statistics. If you’re passionate about solving hard problems at big data scale, knowing that your contribution to Upwork is enabling massive economic value and creating social value globally, then please read on.
The ideal candidate for this role will have experience building machine learning solutions for the Trust and Safety (Financial Fraud) team to tackle business challenges. You don’t have to have a degree from one of the world’s top schools, but you’ve already done a few big things in your career and can hang with some of the brightest data scientists in the world. One of your hallmarks is your ability to reconcile business needs with what the data is suggesting to come up with new ideas and approaches. In the process, you derive much of your joy at work knowing that your inventions and your job matters.
If you and the team you’re on are successful, you will change the company and the world.
- This role is with the Upwork ML Trust and Safety team. The ideal candidate should be a talented and hardworking Senior ML Engineer with expert level knowledge of data science practices, SQL, Python and popular machine learning algorithms and packages to develop innovative trust and safety detection capabilities for our platform.
- You will have the opportunity to work on the vast data sets (structured, unstructured, text, images etc.) in the Trust and Safety domain.
- You will be working on some business problems that may not have clearly defined requirements and need to be able to do independent research to figure out the details, hypothesize and verify, connect the dots to form the big picture. The ability to think out of the box is desired.
- You will work with SQL/Snowflake/Python to explore the existing raw data set against the business problems to be solved, derive the first level of data insights via feature engineering techniques, and potentially instrument more complex features via cross-functional backend development with other supporting teams on a regular basis. Statistical data analysis for both the raw data set and model results is also needed.
- You will experiment with different machine learning packages/algorithms (including but not limited to Classification, Regression, Clustering, Deep Learning, NLP), with a good understanding of the strength/weakness of each algorithm for the problem to solve, and have practical experience with hyperparameter tuning.
- You will be responsible for the full machine learning model training, testing, and final recommendation process, as well as to support the live auditing process.
- You will really impress us if you have a good working knowledge of the AWS Sagemaker machine learning platform and have developed a machine learning pipeline/framework on top of this platform. Experience with Databricks/Apache Spark platform is also appreciated.
- You will need to constantly communicate with the business, analytics, and engineering counterparts to clarify requirements, provide feedback, share the discovered data stories via stats, charts and formal presentations, and finally propose recommendations to maximize the overall business benefit with a controlled cost.
- This is a long term full-time position.
Must Haves (Required skills / qualifications):
- Advanced Python/SQL/Snowflake skills to build end to end machine learning models from raw data exploration, to data cleansing, feature extraction, model training/validation, hyperparameter tuning till production model deployment and monitoring etc.
- Statistical data analysis skills.
- Work independently and with minimal supervision.
- Communicate frequently and effectively in English.
- Deliver high-quality machine learning models with good documentation.
- Be comfortable with multi-tasking and context switches, with proper time management according to the priorities.
- Overlap for at least 4 work hours a day on weekdays with the Upwork team located in California.
- Besides the above, we also highly value your dedication, sense of ownership, and desire to learn and grow.
Come change how the world works.
At Upwork, you’ll shape talent solutions for how the world works today. We are a remote-first organization working together to create exciting remote work opportunities for a global community of professionals. While we have physical offices in San Francisco and Chicago, currently we also support hiring of corporate full-time employees in 19 states in the United States. Please speak with a member of our recruitment team to determine whether you are located in a state in which we are hiring corporate full-time employees.
Our vibrant culture is built on shared values and our mission to create economic opportunities so that people have better lives. We foster amazing teams, put our community first, and have a bias toward action. We encourage everyone to bring their whole selves to work and grow together through development opportunities, mentorship, and employee resource groups. And oh yeah, we've also got amazing benefits - including medical insurance for you and your family, unlimited PTO, 401(k) with matching, 12 weeks of paid parental leave, and a generous Employee Stock Purchase Plan. Check out our Life at Upwork page to learn more about our benefits and the employee experience.
Check out our Life at Upwork page to learn more about the employee experience.
Upwork is proudly committed to recruiting and retaining a diverse and inclusive workforce. As an Equal Opportunity Employer, we never discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical condition), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
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