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We are looking for a Data Scientist with solid experience in analytics and machine learning, particularly within AWS and Amazon SageMaker. You will work on end-to-end ML projects involving structured and unstructured data, developing scalable models and actionable insights for business stakeholders.
Depending on your seniority, you will either contribute as a strong individual contributor (Mid-Level) or drive architectural decisions, stakeholder alignment and mentoring (Senior).
Tecknoworks Context - What You Can Expect
- You'll work in a cross-functional squad with Data Scientists, ML Engineers, Data Engineers, and Project Lead
- Capacity to understand stakeholders, autonomy in execution, and collaborative problem-solving
- Some project industries: Energetic, Life Sciences, FinTech and more.
- Tech ecosystem: AWS and/or Azure cloud, plus Python and SQL
- Delivery: outcome and quality driven
- Access to senior mentorship and a structured growth path, supporting the development and communication skills. Also, we promote a growth mindset, empowering you to learn continuously and take ownership in solving complex, ambiguous problems
- A consultant's mindset and proactive attitude are essential; they allow you to foresee client requirements, spot opportunities, and provide concise, influential, data-based recommendations
- Influence: Aligns multiple stakeholders and supports decision-making
- Perform advanced EDA, correlation analysis, and feature engineering on structured and text data;
- Develop churn prediction models using classification and/or survival analysis techniques;
- Handle class imbalance, calibrate decision thresholds, and design champion/challenger model frameworks;
- Apply interpretability techniques (SHAP, feature importance, PDPs) to identify key drivers and support business recommendations;
- Train, tune, and evaluate models using Amazon SageMaker (built-in algorithms or custom frameworks);
- Design decision-ready dashboards for business stakeholders;
- Define and track relevant KPIs;
- Deliver data-driven storytelling tailored to non-technical audiences;
- Experience with BI tools is considered a plus;
- Ensure data and model versioning, testing, and reproducibility;
- Implement data quality checks and validation logic;
- Support lightweight deployment and monitoring of ML models (batch or near real-time);
- Work with AWS services such as S3, Glue, Lambda, and SageMaker Pipelines;
- Step Functions or Airflow experience is a plus
Mid-Level Expectations
- 3+ years' experience in data science with end-to-end ML delivery
- Strong Python skills (pandas, scikit-learn)
- Solid SQL knowledge (preferably Redshift)
- Practical experience with AWS (S3, Glue, Lambda, SageMaker)
- Good communication skills and ability to work autonomously
- Ability to translate data into clear insights for non-technical stakeholders
- 5+ years' experience with advanced ML and solution ownership
- Experience leading solution design, modelling strategy, and cross-team alignment
- Ability to mentor junior/mid colleagues
- Experience with ML workflow orchestration (SageMaker Pipelines, Airflow, Step Functions)
- Exposure complex pipelines and large-scale data architectures
- Proven track record of influencing technical decisions
- Previous exposure to energy sector industry (pricing models, contracts, switching behavior, seasonality, regulated price changes)
- Exposure to environments where clarity of thought, accountable execution, and thoughtful collaboration naturally shape how work gets done. Individuals who navigate complexity with a structured approach, treat outcomes with care, and remain open to refining their ideas through learning, and diverse perspectives tend to feel most at home in this role
Key Skills
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