Senior Data Scientist

Top 3 reasons to join us

• Competitive salary, healthy and sporty environment
• Hybrid/remote professional working environment
• Opportunity traveling to US for training

Job description

About the Role
We are looking for an exceptional Data Scientist who can transform large-scale business data into predictive models, intelligent recommendations, and measurable business outcomes.
This role sits at the intersection of machine learning, analytics, forecasting, optimization, AI systems, and business strategy. You will work on complex problems across SaaS, e commerce, customer intelligence, agriculture technology, forecasting, operations, and AI-powered decision-support systems.
You will partner closely with Product Managers, Engineers, AI Engineers, Data Engineers, Operators, and Business Leaders to build models and data products that directly improve revenue, efficiency, customer experience, planning accuracy, and strategic decision-making.
We are looking for someone who combines strong statistical foundations, machine learning expertise, business judgment, and the ability to turn messy real-world data into reliable, actionable intelligence.
What You’ll Do
Machine Learning, Forecasting & Decision Intelligence
Design, develop, validate, and deploy machine learning models and analytical systems that improve business planning, prediction, and decision-making.
You may work on use cases such as:
● Forecasting
○ Sales forecasting
○ Demand forecasting
○ Inventory forecasting
○ Yield forecasting
○ Harvest forecasting
● Customer intelligence
○ Customer behavior prediction
○ Churn prediction
○ Lead scoring
○ Customer lifetime value modeling
○ Customer segmentation
● Recommendations and ranking
○ Product recommendations
○ Customer recommendations
○ Ranking models
○ Prioritization systems
○ Next-best-action recommendations
● Risk and anomaly detection
○ Anomaly detection
○ Risk scoring
○ Early-warning systems
○ Signal detection
○ Outlier detection
● Optimization
○ Replenishment optimization
○ Allocation optimization
○ Pricing optimization
○ Inventory optimization
○ Resource optimization
● Decision support
○ Scenario modeling
○ What-if analysis
○ Decision-support tools
○ Prescriptive analytics
○ Tools that help teams choose better actions
You will be expected to move beyond prototypes and help bring models into production where they can be monitored, improved, and used by real teams. A key part of this role is translating predictions into decisions and measurable business impact.
Data Science & Analytics
Analyze large, complex datasets to identify patterns, opportunities, risks, and business insights.
You will:
● Perform statistical analysis and hypothesis testing
● Build experimentation frameworks
● Design and evaluate A/B tests
● Develop predictive and prescriptive analytics solutions
● Identify drivers of customer, product, and business performance
● Create actionable recommendations rather than simply reporting metrics
● Quantify business impact, uncertainty, and tradeoffs
AI & Applied Generative AI
Apply modern AI techniques to practical business problems.
You may work on:
● AI-powered decision-support systems
● Retrieval-augmented intelligence systems
● Agentic workflows powered by data and AI
● LLM-based classification, summarization, extraction, and recommendation systems
● AI evaluation frameworks
● Knowledge graph intelligence
● Signal detection and event prediction
● AI-assisted internal tools for business users
We value candidates who know how to use AI tools to accelerate their own work while still applying strong judgment, validation, and scientific rigor.
Product & Business Collaboration
Partner with Product, Engineering, Operations, Marketing, Sales, Finance, and Leadership teams.
You will:
● Translate business challenges into measurable data science problems
● Define KPIs, success metrics, and model performance standards
● Communicate findings to both technical and non-technical stakeholders
● Help teams understand not only what the data says, but what they should do next
● Prioritize work based on expected business value
● Balance analytical rigor with speed and practical execution
Data Engineering & MLOps Collaboration
Work with Data Engineers and AI Engineers to build scalable, reliable data and modeling infrastructure.
You will help:
● Design clean, reusable datasets
● Build and improve feature stores
● Define data quality checks
● Create robust data pipelines for training and inference
● Monitor model drift and performance degradation
● Establish automated evaluation and retraining processes
● Improve model reliability, observability, and maintainability
Example Projects
You may work on projects such as:
Retail & E-Commerce
● Sales forecasting
● Inventory optimization
● Replenishment recommendations
● Product recommendation engines
● Search relevance optimization
● Dynamic pricing models
● Customer segmentation
● Customer lifetime value prediction
● Promotion and pricing effectiveness analysis
● Returns, fraud, or anomaly detection
Agriculture SaaS
● Yield prediction
● Harvest forecasting
● Crop planning optimization
● Weather impact analysis
● Resource allocation recommendations
● Farm performance benchmarking
● Risk scoring and early-warning systems
● Operational decision-support tools
SaaS & Customer Intelligence
● Churn prediction
● Lead scoring
● Account health scoring
● Customer journey analysis
● Product usage modeling
● Expansion and upsell recommendations
● Customer support intelligence
● Retention and engagement models
AI Systems
● Retrieval-augmented intelligence
● Agent evaluation frameworks
● AI-assisted business decision systems
● Knowledge graph intelligence
● Signal detection and event prediction
● Automated insight generation
● AI workflows for internal teams

Your skills and experience

Required Qualifications
● Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Economics, Engineering, or another quantitative field
● 5+ years of experience in Data Science, Machine Learning, Applied AI, or closely related roles
● Strong knowledge of:
○ Statistics
○ Probability
○ Experimental design
○ Machine learning
○ Predictive modeling
○ Forecasting or optimization
● Strong proficiency in:
○ Python
○ SQL
● Hands-on experience with common data science and ML tools, such as:
○ Pandas
○ NumPy
○ Scikit-learn
○ XGBoost
○ LightGBM
○ TensorFlow or PyTorch
● Experience working with large datasets and cloud-based data platforms
● Ability to frame ambiguous business problems as analytical or modeling problems
● Strong communication skills with both technical and non-technical audiences
● Strong problem-solving skills and a bias toward practical business impact

Preferred Qualifications
● Experience with LLMs and Generative AI
● Experience with agentic AI frameworks
● Experience with MLOps, model monitoring, and production ML systems
● Experience with feature stores
● Experience with forecasting and optimization models
● Experience with recommendation systems or ranking models
● Experience with customer intelligence systems
● Experience with BigQuery, Snowflake, Databricks, or similar platforms
● Experience in:
○ SaaS
○ e-commerce
○ agriculture technology
○ marketplaces
○ retail
○ operations-heavy businesses
● Experience building tools, models, or data products used directly by business teams or customers

Technical Skills
Programming
● Python
● SQL
● R optional
Machine Learning
● Classification
● Regression
● Clustering
● Time-series forecasting
● Recommendation systems
● Ranking models
● Deep learning
● Reinforcement learning a plus
● Model evaluation and validation
● Experimentation and causal inference a plus
Data Platforms
● BigQuery
● Snowflake
● Databricks
● PostgreSQL
● MySQL
AI & MLOps
● MLflow
● Airflow
● Docker
● Kubernetes
● LangChain
● LangGraph
● Vector databases
● Feature stores
● Model monitoring tools
Visualization & Data Products
● Tableau
● Power BI
● Looker
● Streamlit
● Dash or similar tools

What Success Looks Like
Within your first 12 months, you will:
● Deploy multiple production-ready machine learning or AI models
● Improve forecasting accuracy and business decision quality
● Build data products actively used by business teams
● Create measurable revenue growth, cost savings, efficiency gains, or customer experience
improvements
● Establish reusable modeling, experimentation, and evaluation frameworks
● Help improve the company’s data quality, model reliability, and AI readiness
● Help shape an AI-first, data-driven decision-making culture

What We Value
We are building a culture around:
● Data-driven decision-making
● AI-first thinking
● Ownership and accountability
● Continuous learning
● Bias toward action
● Scientific rigor
● Clear communication
● Practical business judgment
● Building solutions that create measurable impact

Bonus Points
● Built production AI or ML systems used by real customers or business teams
● Built forecasting systems at scale
● Built recommendation engines or ranking systems
● Worked with AI agents or autonomous workflows
● Kaggle competition achievements
● Published research, technical articles, or open-source contributions
● Demonstrated use of AI tools such as ChatGPT, Claude, Gemini, Cursor, Lovable, or similar tools to accelerate analysis, experimentation, coding, and delivery
● Experience turning ambiguous business problems into shipped data products

Why you'll love working here

Why Join Us?

• AI Playground: Work in an environment where your AI knowledge isn’t just welcomed—it’s celebrated. Test and deploy AI solutions that make a real impact.
• Growth Opportunities: Mentorship from senior IT pros, access to online training (e.g., Udemy), and a clear path to roles like Systems Admin or AI Specialist.
• Young & Dynamic Team: Collaborate with peers who share your passion for tech and innovation.
• Perks: Competitive salary, health benefits and specific company perks, e.g., “free coffee,” “free buffet lunch”, “yoga” , “football” ..etc...