Machine Learning Engineer
SweedSalary not specified
Full Time
Mid-level (3 to 4 years)
Not SpecifiedCompensation
Mid-level (3 to 4 years)Experience Level
Full TimeJob Type
UnknownVisa
TechnologyIndustries
Requirements
- Master’s degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Data Science) or equivalent experience
- 3+ years of professional experience in data science or applied machine learning
- Strong problem-solving and analytical skills, with the ability to turn complex product questions into actionable insights
- Excellent communication skills, both verbal and written, with the ability to present technical results to non-technical audiences
- Proven ability to build and maintain strong relationships with stakeholders across teams and functions
- Deep understanding of machine learning algorithms, from classical methods (e.g., regression, random forests, k-means) to advanced techniques (e.g., gradient boosting frameworks such as XGBoost, LightGBM, CatBoost, and transformer-based architectures like BERT or Sentence Transformers)
- Proficiency in Python or R, and data manipulation tools/libraries such as Pandas and SQL
- Hands-on experience deploying models in production and managing ML lifecycle processes (monitoring, retraining, version control)
Responsibilities
- Data Analysis & Research: Analyze large datasets using queries and scripts to extract meaningful insights and identify opportunities for improving complex ML and bidding systems
- Simulation & Modelling: Design and execute simulations to validate hypotheses, quantify efficiency gains, and model system performance
- Experimentation & Causal Inference: Develop robust experiment designs and metric frameworks to deliver unbiased, data-backed insights for product and business decisions
- End-to-End ML Deployment: Build, train, and deploy ML models into production environments, managing the full lifecycle including versioning, monitoring, and retraining
- Scalability & Performance Optimization: Operationalize ML models at scale, optimizing for performance, reliability, and cost efficiency in real-world production systems
- Cross-Functional Collaboration: Work closely with product, engineering, and data teams to translate business problems into analytical solutions
Skills
Machine Learning
SQL
Python
Data Analysis
Simulation
Causal Inference
ML Deployment
MLOps
Experiment Design
ShyftLabs
Data-driven decision-making solutions for organizations
About ShyftLabs
ShyftLabs helps organizations adopt a data-first approach to their decision-making processes. Their services focus on establishing systems that enable companies to make quicker and more informed decisions based on data analysis. This approach allows businesses to gain insights that can keep them ahead of their competitors. Unlike other companies that may offer generic consulting services, ShyftLabs emphasizes the importance of data in driving decisions, ensuring that organizations can leverage their data effectively to enhance their strategic planning and operational efficiency.
None, CanadaHeadquarters
2018Year Founded
VENTURE_UNKNOWNCompany Stage
Data & Analytics, ConsultingIndustries
11-50Employees
Benefits
Health Insurance
Hybrid Work Options
Professional Development Budget
Risks
Increased competition from startups offering innovative, cost-effective solutions.
Growing demand for in-house analytics teams reducing reliance on consultants.
Rapid AI advancements may outpace ShyftLabs' current technology offerings.
Differentiation
ShyftLabs specializes in data governance, warehousing, and predictive analysis services.
The firm empowers organizations with a data-first approach for decision-making.
ShyftLabs establishes processes for faster, insightful decisions to outpace competition.
Upsides
Increased demand for data governance due to stricter privacy regulations.
Growing interest in predictive analytics in retail for inventory optimization.
Rising adoption of cloud-based BI tools among SMEs for cost-effectiveness.
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