Enterprise Data Analyst Professional
9 godzin temu
">This role is based in our Warsaw 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 your recruiter can share more about the in-office requirements.
">Our employees in Poland are employed under a contract of employment. At Asana, we're committed to building teams that include a variety of backgrounds, perspectives, and skills, as this is critical to helping us achieve our mission. If you're interested in this role and don't meet every listed requirement, we still encourage you to apply.
Key Responsibilities:
- Partner closely with Product, Business, Design, Engineering, and other members of the Data team to drive business outcomes.
- Develop and implement data-driven strategies to inform product development and decision-making.
- Collaborate with cross-functional teams to design and analyze experiments and causal analysis to ship impactful product changes.
- Communicate complex data insights and recommendations to both technical and non-technical audiences.
Requirements:
- Bachelor Degree in Math, Statistics, Economics, Computer Science, Engineering, or related quantitative field.
- 4+ years of experience in applying data science techniques to drive technical product development and decision-making.
- Strong technical background in statistics, math, computer science, information science, or another quantitative field.
- Fluency in at least one data processing and programming language (e.g., Python, R), relational data modeling and SQL, familiarity with distributed data processing systems (e.g., Spark, Redshift).
- Expertise in statistical methods and experimental design and analysis.
- Background in advanced statistical modeling (e.g., GLM, mixed effects) and/or machine learning.
- Excellent written and verbal communication skills.
- Ability to collaborate effectively with peers and leaders across data science, design, and engineering organizations.
Benefits
- Private healthcare.
- Mental health care.
- Sport subscription.
- Training budget.
- Coaching.
- Long-term savings or retirement plans.
- Free coffee.
- Canteen.
- Modern office.
- No dress code.
- In-house trainings.
- In-office culinary options.
- Free lunch.
- Free snacks.
- Shower.
- Bike parking.
- Free beverages.
- Free breakfast.
- Startup atmosphere.
- In-house hack days.
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