About us
Tameson is a global e-commerce business helping SMEs source industrial components. We are looking for a Marketing Analytics Specialist to join our Data Analytics team and bring statistical rigor to how we run and measure paid advertising.
The role
This is a hands-on ownership role where data and marketing meet. You set our paid acquisition strategy, make the business case, run the campaigns and prove the results with data. What matters most to us: a sharp analytical mind, a quantitative background, being at home with data tools and technology, and a good sense for what drives the business. Experience with Google Ads or other ad platforms is a plus, but platforms can be learned. The analytical and entrepreneurial mindset is much harder to teach.
You will work directly with the Data Analytics Lead and Marketing Lead in a team that sits at the center of how Tameson makes decisions. Your output will materially shape where marketing budget goes, how we measure its return, and how we test new ideas.
A flavor of recent work
Built a Markov-chain multi-touch attribution model to improve our early-funnel bidding in paid ads channels.
Built LTV and cohort retention models that let us value customer segments differently, and feed those values back into bidding and targeting.
Designed and ran A/B and CRO experiments to validate channel changes, on-site changes, and bidding strategies.
Built the BigQuery and Looker Studio reporting layer that the business actually uses to track CAC, POAS, and LTV.
Designed an experimental framework to measure the incremental effect of paid advertisement spend.
What you would pick up
Extend and improve our attribution work, challenge assumptions, try alternative models, quantify uncertainty.
Take full ownership of our paid advertisement strategy, based on data insights and experimental results.
Design experiments end-to-end: A/B, geo, holdout, CRO. All with proper power analysis, randomization, and evaluation.
Build models that inform capital allocation across channels: saturation curves, diminishing returns, MMM, for example.
Analyze complex datasets across customer journeys, search query data, marketplace performance, and CRM.
Develop customer and audience models that inform who we should target, where, and how much we should pay to reach them.
