Data Analyst, Operations Planning
Навыки
- Внимание к деталям
- Ответственность за результат
- Решение задач
- Python
- SQL
- Временные ряды
Задачи
- Partner with Product, Engineering, Data Science & Analytics, Operations, Finance and other cross-functional stakeholders on initiatives to improve operational performance
- Develop frameworks and scalable processes to streamline reporting, drive decision-making and prioritization
- Forecast operational requirements needed to maintain high service levels and meet contractual and financial targets
- Work with our bike & scooter share markets to deliver ongoing support and deep dive analyses on performance
- monitor and diagnose performance and present findings to key stakeholders
- Collaborate with cross-functional teammates to tackle complex problems including: asset maintenance, system health and labor forecasting
- Experience
- Bachelor's Degree or equivalent relevant professional experience
- Highly skilled in SQL and quantitative analysis
- You can deep dive into large amounts of data, draw meaningful insights, dissect business issues and draw actionable conclusions
- Ability to develop scalable approaches and produce data visualizations to drive business insights and provide tangible solutions
- experience building dashboards for performance analysis is a plus
- Extreme comfort working with ambiguity. Ability to translate unclear issues or unstructured problems into clearly defined requirements with minimal oversight
- Strong interpersonal skills, with the ability to build relationships, trust and influence with cross-functional partners
- Strong attention to detail, structured thinking and experiences developing processes to reduce human error
- Adept at contextualizing real world operations into analytical problem solving
- Passionate about sustainable mobility and active transportation
- A strong sense of product ownership - you’re constantly looking for ways to improve the customer’s experience and aren’t afraid to get your hands dirty to do so
- Bonus: Proficiency in Python and associated data science libraries
- At Lyft, our purpose is to serve and connect
- We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive
- Data and analytics are at the heart of Lyft's products and decision-making
- As a member of the Lyft Urban Solutions team, you will play a key role in shaping the future of bike & scooter share by leveraging data to improve the performance of our bikeshare and scooter markets across America
Требования
- 3+ years experience in data analytics in a high-growth environment, preferably a consulting, operations or transportation / logistics space
- Great communication (listening, written, and oral) skills with the ability to present findings & recommendations targeted to the audience in question
Условия
- Great medical, dental, and vision insurance options with additional programs available when enrolled
- Mental health benefits
- Family building benefits
- Child care and pet benefits
- 401(k) plan with company match to help save for your future
- In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
- 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
- Subsidized commuter benefits
- Monthly Lyft credits and complimentary Lyft Pink membership
- Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging
- All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law
- We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law
- Lyft highly values having employees working in-office to foster a collaborative work environment and company culture
- This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays
- Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role
- Your recruiter can share more information about the various in-office perks Lyft offers
- Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid
- The expected base pay range for this position in the New York City area is $82,800 - $103,500, not inclusive of potential equity offering, bonus or benefits
- Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location
- Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process
Паспорт вакансии
История публикации
Появилась в Вакандии30 дней
Перепубликациинетпубликовалась один раз
Проверяли на источникеВидели 30 дней назад
Среди похожихНет данных144 из 30 · у похожих вакансий почти одинаковый возраст — сравнивать нечего
Откуда что взялось
Отмечено то, что вывели мы. Без пометки — значение назвал работодатель.
ГрейдMiddleвыведено из другого признака
Формат работыГибридвычитано из текста вакансии
ГеографияНью-Йорк, СШАвычитано из текста вакансии
Зарплата82 800 USD — 103 500 USD в годвычитано из текста вакансии
Почему на этом месте в выдаче
Порядок выдачи объявлен контрактом: свежесть решает между днями, полнота и зарплата — внутри дня.
Полнота карточки1004 из 4 полей: грейд, формат, география, зарплата
Зарплата названа100вилку назвал источник
Проверка Вакандии
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- Кадровое агентство
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- greenhouseОсновная публикация · 2026-05-28
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Признаки мошенничества
- Просят предоплату, «залог» или деньги за обучение и оборудование.
- Требуют код из SMS, данные банковской карты или доступ к «Госуслугам».
- Быстро уводят в мессенджер и торопят с решением.
- Обещают большой доход без опыта и без деталей задач.
Настоящий работодатель не просит денег и платёжных данных до трудоустройства.
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