Applied Scientist IV

Applied Scientist IV

Location: Seattle, WA ONSITE

Duration: 9 Months

(Possible Extension)

(phone number removed)

Job Description: The Campaign Measurement & Optimization (CMO) organization is looking for a Data Scientist (contract) interested in solving one of the most challenging business problems in marketing measurement and developing cutting-edge Client model. Working with our team of data scientists, applied scientists, research scientists, and economists, the scientist will design and develop scalable marketing measurement at Client and its subsidiaries.

The Campaign Measurement & Optimization (CMO) organization's mission is to be the most trusted source of measurement science solutions to drive marketing investment decisions across Client. The CMO team provides incrementality and efficiency measurement services to the marketing stakeholders across Client's lines of business, including Stores, Prime Video, Client Devices, Alexa, Client Business, Client Music, Client Fresh, as well as subsidiaries including Audible, Ring, Whole Foods, and more. CMO applies industry leading deep learning based causal inference models to measure omni-channel effectiveness of marketing campaigns from these businesses worldwide. The impact and influence of the organization is tremendous, helping optimize spend decisions on a scale that exceeds many countries' GDP. Our outputs shape Client product and marketing teams' decisions and therefore how Client customers see, use, and value their experience with Client.

This is a high-impact role with opportunities to develop systems and analyze marketing effectiveness that contributes billions of dollars to the business. As an Data Scientist, you will be responsible for development and production deployment of the cutting edge measurement and optimization models, while collaborating with businesses, marketers, and software teams to solve key challenges facing the teams. Such challenges include measuring the incremental impact of multi-channel marketing portfolios, estimating the impact on sparse customer actions, and scaling measurement solutions for WW marketplaces. Unlike many companies who buy existing off-the-shelf marketing measurement systems, we are responsible for studying, designing, and building systems to serve Client's suite of businesses. Our team members have an opportunity to be on the forefront of marketing measurement thought leadership by working on some of the most difficult problems in the industry with some of the best product managers, scientists, economists and software developers in the business.

Responsibilities Include:

- Employ reduced form causal econometric methods to estimate the treatment effects of marketing.

- Utilize model estimates to provide insights to customers and drive business decisions.

- Build statistical models and tools using technical knowledge in causal inference, machine learning, statistical modeling, and other quantitative techniques.

- Understand the business reality behind large sets of data and develop meaningful analytic solutions.

- Innovate by adapting to new modeling techniques and procedures.

- Utilizing code (Python, R, etc.) for analyzing data and building statistical models to solve specific business problems

- Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters

- Collaborate with researchers, software developers, and business leaders to define product requirements and provide analytical support

- Communicating verbally and in writing to business customers and leadership team with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations

Business Group/Team:

- Collaborative team- opportunities to work with teammates from different job families (economists, other data scientists/ engineers, product manager, and some leadership),

- Cross team collaboration - Most of the time this contractor will be working with team on the measurement side but will also meet frequently with stakeholders; to review results, help with designs, answer questions, etc)

key projects:

This person will be helping own the team own their Regular Measurement for US and help to do some saturation analyst/ study for US. This person will take over all the operation and a little research science work for US market places.

Reason of request?

Last Mile 2024 US Saturation Analysis + SyRT Q1-Q3'24 measurement support while the team sets up the new UK van marketing region, randomization design and H2'24 measurement

Interaction with team/Day to Day:

- Weekly stand up, stakeholder sync ups.

Extension? TBD

Conversion? n/a

Training/ Ramp- up

1-2 month for onboarding. There will be an onboarding buddy to help ramp up on what the team is working, what the stakeholder is looking for, what are the model, the measurement solutions they have in production.

Role interesting:

- Work on projects where they can learn about experiment design and Causal Inference study.

- This role had opportunity to collaborative w/ different functional teams and directly work with stakeholders.

- The project the team works on has high visibility and impact

Leadership principles:

- Ownership

- Curious & Learn

REQUIRED SKILLS

- Be able to communicate to stakeholders

- Causal Inference experience or experience on Causal Inference projects

- Experiment Design (A/B testing, Randomized controlled trial (RTC), etc)

- BA in Economics or similar field + 3yrs of industry experience

- Marketing measurements

PREFERRED SKILLS

- PHD background in economics candidates would be great (no industry experience if they have just this)

- If they have Deep Learning or AI that is great but no needed for this role

Best/ Avg:

Bes: PHD background in economics, who specialized in experimental design. No industry experience needed if they have PHD. If no PHD will need to have at least 3+ yrs. of experience with causal influence, marketing measurements, and experiment designs.

Avg: They know the general idea of the experiment design and understand the technics. Be able to understand the basics.

Causal Inference experience or experience on Causal Inference projects

Experiment Design (A/B testing, Randomized controlled trial (RTC), etc)

Python - This is used a lot on the team.

Candidate Review & Selection