Applied Scientist IV - Intelliswift Software Inc
Seattle, WA 98109
About the Job
Pay rate range - $90/hr. to $92/hr. on W2
Must Have
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.
Title: Data scientist, Research/Applied scientist, and economist, open to Machine learning engineer former experience.
Yrs Exp: 3+
REQUIRED SKILLS
Best/ Avg:
Bes: Ph.D. background in economics, who specialized in experimental design. No industry experience is needed if they have PHD. If no Ph.D. 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.
Role interesting:
This person will be helping own the team own their Regular Measurement for US and help to do some saturation analysis/ study for US. This person will take over all the operations and a little research science work for US marketplaces.
This is a high-impact role with opportunities to develop systems and analyze marketing effectiveness that contributes billions of dollars to the business.
As a Data Scientist, you will be responsible for the development and production deployment of 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 a suite of businesses.
Our team members have an opportunity to be at 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
Must Have
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.
Title: Data scientist, Research/Applied scientist, and economist, open to Machine learning engineer former experience.
Yrs Exp: 3+
REQUIRED SKILLS
- Be able to communicate with stakeholders
- Causal Inference experience or experience on Causal Inference projects
- Experiment Design (A/B testing, Randomized controlled trial (RTC), etc)
- BA in Economics or a similar field + 3 years of industry experience
- Marketing measurements
- 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: Ph.D. background in economics, who specialized in experimental design. No industry experience is needed if they have PHD. If no Ph.D. 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.
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
This person will be helping own the team own their Regular Measurement for US and help to do some saturation analysis/ study for US. This person will take over all the operations and a little research science work for US marketplaces.
This is a high-impact role with opportunities to develop systems and analyze marketing effectiveness that contributes billions of dollars to the business.
As a Data Scientist, you will be responsible for the development and production deployment of 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 a suite of businesses.
Our team members have an opportunity to be at 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
Source : Intelliswift Software Inc