Data Scientist

Asana

+2
+6
United States

+2
+6
United States

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À propos

The Data Science team at Asana is pivotal in fulfilling our mission by fostering a data-driven approach in shaping both our product and business strategies. In your role on the Marketing Data Science team, you will be the deepest technical expert responsible for using data and scientific techniques to design and build scalable, state-of-the-art solutions to enhance Asanas marketing effectiveness. You will drive the technical roadmap for data science, collaborating with marketing leadership and the broader Asana data community to uncover new opportunities. You will provide technical leadership and hands-on mentorship, elevating the team's technical bar and influencing overall business strategy through best-in-class modeling and experimental design. This role is based in our San Francisco office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements. What youll achieve: Architect, design, and lead the technical execution for the Marketing Data Science roadmap, serving as the Solution Architect for all core projects including Media Mix Modeling (MMM), User Lifetime Value, Causal Inferences, Multi-touch Attribution, and Spend Optimization engines. Act as the primary technical subject matter expert for the Marketing Data Science team, setting the technical bar for modeling quality, code rigor, data pipeline architecture, and solution scalability. Collaborate with marketing leadership to pinpoint how data science can be further integrated into Asana's business approach. Provide hands-on technical mentorship and guidance to a team of data scientists at varying levels, helping them navigate complex modeling challenges, choose appropriate methodologies, and establish robust ML Ops. Develop and standardize MLOps tooling and processes that enable the team to deploy, monitor, and maintain multiple models in production efficiently and reliably. Research, prototype, and advocate for emerging capabilities and state-of-the-art models in the marketing data science space, demonstrating their potential benefits and leading their implementation. Take on a technical leadership role within the broader Asana Data Community, interacting with Data Engineering and Platform teams to influence the data and MLOps infrastructure required to support marketing data products. About you: Bachelor Degree in Math, Statistics, Computer Science, Engineering a related quantitative field, or equivalent experience 6+ years of experience in a data science role, with 2+ years dedicated to technical leadership and mentorship of other data scientists, successfully driving the architecture and execution of large-scale production data science projects 4+ years of experience collaborating with Marketing functions on deep technical projects, with extensive experience designing, implementing, and deploying marketing models (e.g. MMM, LTV, MTA, Uplift) Expert-level knowledge in advanced statistical modeling, causal inference, experimental design and analysis, and machine learning techniques relevant to marketing effectiveness Proven track record developing, deploying, and maintaining scalable production ML solutions and data products Demonstrated curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making Technical Stack: Expert proficiency in SQL and Python. Experience with MLOps tools (e.g., MLFlow), statistical languages (e.g., R), and distributed data processing systems (e.g., Spark, Redshift) is a plus Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making. What well offer: Our comprehensive compensation package plays a big part in how we recognize you for the impact you have on our path to achieving our mission. We believe that compensation should be reflective of the value you create relative to the market value of your role. To ensure pay is fair and not impacted by biases, we're committed to looking at market value which is why we check ourselves and conduct a yearly pay equity audit. For this role, the estimated base salary range is between $248,000 - $316,000. The actual base salary will vary based on various factors, including market and individual qualifications objectively assessed during the interview process. The listed range above is a guideline, and the base salary range for this role may be modified. In addition to base salary, your compensation package may include additional components such as equity, sales incentive pay (for most sales roles), and benefits. If you're interviewing for this role, speak with your Talent Acquisition Partner to learn more about the total compensation and benefits for this role. We strive to provide equitable and competitive benefits packages that support our employees worldwide and include: Mental health, wellness & fitness benefits Career coaching & support Inclusive family building benefits Long-term savings or retirement plans In-office culinary options to cater to your dietary preferences These are just some of the benefits we offer, and benefits may vary based on role, country, and local regulations. If you're interviewing for this role, speak with your Talent Acquisition Partner to learn more about the total compensation and benefits for this role. #LI-AA1 About us Asana helps teams orchestrate their work, from small projects to strategic initiatives. Millions of teams around the world rely on Asana to achieve their most important goals, faster.

Compétences idéales

Experimental Design
Machine Learning
Python
Redshift
SQL
Spark

United States

Expérience professionnelle

Machine Learning
Data Scientist

Compétences linguistiques

English

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