For more than 20 years,
TradeBeyond
has been advancing a more efficient, responsible supply chain. Responding to retail sourcings need for smarter, automated workflows and data transparency, we developed the industrys leading supply chain platform, CBX, which is relied on by many of the largest brands and retailers around the world.
As consumers, businesses, and governments alike have increased their commitment to sustainability, Fortune 500 companies such as The Home Depot, REI, Safeway, Lidl, and Lululemon have turned to TradeBeyond to help optimize product development, manage suppliers, reduce waste, and improve quality and compliance.
Learn more at
About the Role
You will own the analytics lifecycle from raw data to decision-grade insights and ML/DL models. You will clean and model datasets in our cloud-based environment with strong SQL, engineer features, run experiments and statistical analysis, and build reliable dashboards for product and operations stakeholders. This role absorbs day-to-day data work - data prep, modeling exploration/optimization, and reporting. Emphasis on data quality, clear metrics, and actionable recommendations.
Responsibilities:
- Data foundations & quality:
Ingest, clean, standardize, and document data; implement reproducible SQL transforms and maintain metric definitions/data dictionaries in a cloud-based stack. - Modeling & insights:
Explore and tune ML/DL models; deliver interpretable findings and recommendations. - Experimentation & metrics:
Design/deliver A/B testing and multivariate tests with Eng./PM; define north-star and guardrail metrics; build and maintain dashboards and monitors. - Handoffs:
Provide well-structured artifacts (features, datasets, eval. samples) to support downstream productionization.
Requirements
- 3 years+
Data science & statistics background and/or professional experience
(e.g., DS / statistics / econometrics) with applied analytical work. - Strong
Python
and
SQL
for reproducible analyses, version control (
Git
) and clear written documentations. - Hands-on
ML
/DL. Feature engineering & hyperparameter tuning for:
logistic
,
XGBoost
,
t
ime series,
DNN
,
RNN
,
transformers
, etc.; basic model explainability (e.g.
SHAP
or equivalent). - Proven
engineering collaboration
on
API specifications
,
data schemas
, and
golden sets
/
test data
with data-quality checks. - BI & monitoring basics:
proficiency in
Looker
,
Tableau
, etc.; familiarity with Ops monitoring via
Superset
/
Prometheus
/
Grafana
etc. - Languages:
native Mandarin
and
business-level English
.
Nice-to-Have
- Workflow tooling for data tasks:
dbt
and one orchestrator (
Airflow
/
Prefect
). - Storage / data engines: modeling & performance tuning on
PostgreSQL
/
MongoDB
/
Elasticsearch
or a cloud data warehouse (e.g.,
Redshift
/
BigQuery
). - LLM exposure: prior exposure to
LLM APIs
(OpenAI / Gemini / Azure / Bedrock) for prototyping. - Domain familiarity: supply-chain / manufacturing / e-commerce data.
TradeBeyond
Offers
You will work in a flat and open team environment where your experience and expertise are valued. You will work in partnership with a leadership team who have profound domain knowledge in their functional areas and are keen to work with you to continuously make positive impacts for our customers and employees. Externally, you will be engaging with a client network on a global footprint.
We offer competitive compensation in a dynamic, high growth and global environment. At TradeBeyond, we value the diversity of our employees and partners. We believe that our company thrives when we support and celebrate our differences.
Interested parties, please apply together with resume, stating current and expected salary, and send it via APPLY NOW.
We are an equal opportunity employer and welcome applications from all qualified candidates. All information provided by applicants will be treated in strictest confidence and handled confidentially for recruitment-related purposes within the company and our associated company. Applicants may be considered for other suitable positions within the company over a one-year period, after which their personal data will be destroyed