Applied Science Manager, Trust

Company Description

LinkedIn is the worlds largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. Were also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture thats built on trust, care, inclusion, and fun where everyone can succeed.

Job Description

The Trust Data Science team delivers insights, metrics, and data solutions as part of the cross-functional Trust team to realize its mission to create safe, trusted, professional experiences where individuals and organizations can be productive and successful. Nested within Trust DS, the Applied Science team solves some of the complex problems of measurement and experimentation with research and cutting edge technologies. As the manager of this team, you lead and grow a world-class group of applied scientists and statisticians, fostering a culture where scientific rigor meets product velocity.

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.

Responsibilities

Work with a team of high-performing data science professionals, and cross-functional partners (product, engineering, AI, policy, and operations) to build high fidelity abuse prevalence and false positive metrics using advanced algorithms and models
Build and operate advanced experimentation methodologies (holdouts, network / adversary aware testing frameworks, variance reduction) so product and AI/Eng teams can measure impact quickly and rigorously
Deploy approaches like ML assisted sampling, anomaly detection, and automated root-cause analysis to drive agility in identifying and mitigating emerging abuse patterns
Work with the team and cross-functional partner to identify business opportunities and develop inference, algorithms, models and experimentation methodologies to address them
Lead the team to conduct in-depth and rigorous causal analysis and develop causal methodology and machine learning models to drive member value
Guide the team to explore vast datasets to discover relevant features and attributes that can improve the performance of existing models.
Extract valuable information from unstructured data sources and apply feature engineering techniques to enhance model effectiveness
Continuously optimize and fine-tune models to meet business objectives and user expectations.
Promote and enable adoption of technical advances in Data Science; elevate the art of Data Science practice at LinkedIn
Act as a thought partner to senior leaders to prioritize/scope projects, provide recommendations and evangelize data-driven business decisions in support of strategic goals
Partner with cross-functional teams to initiate, lead or contribute to large-scale/complex strategic projects for team, department, and company
Provide technical guidance and mentorship to junior team members on solution design as well as lead code/design reviews


Qualifications

Basic Qualifications

Bachelor's Degree in a quantitative discipline: Statistics, Operations Research, Computer Science, Informatics, Engineering, Applied Mathematics, Economics, etc.
1+ years of management experience or 1+ years of staff level engineering experience with management training
5+ years of relevant work experience Background in at least one programming language (eg. R, Python, Java, Scala/Spark)
Experience in applied statistics and statistical modeling in at least one statistical software package, (eg. R, Python)


Preferred Qualifications

Master's degree or PhD in quantitative fields, such as Economics, Statistics, Operations Research, Computer Science, Informatics, Engineering, Applied Mathematics.
2+ years of hands-on software engineering/technical management and people management experience
7+ years industry experience in software design, development, and algorithm related solutions.
Research experience related to one of the following domains: Experimentation and Causal inference, Machine Learning, Econometrics, Operations Research, or related area, with publications in conferences
3+ years experience working in Trust & Safety domain, particularly in adversarial abuse


Suggested Skills

Machine Learning
Experimentation
Causal Inference


You will Benefit from our Culture

We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $164,000.00 to $268,000.00 Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor. The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linkedin.com/benefits.mr3

Additional Information

Equal Opportunity Statement

We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.

LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful.

If you need a reasonable accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us at accommodations@linkedin.com and describe the specific accommodation requested for a disability-related limitation.

Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to:

Documents in alternate formats or read aloud to you
Having interviews in an accessible location
Being accompanied by a service dog
Having a sign language interpreter present for the interview


A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response.

LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information.

San Francisco Fair Chance Ordinance

Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records.

Pay Transparency Policy Statement

As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.

Global Data Privacy Notice for Job Candidates

Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedin.com/candidate-portal.