Turn massive enterprise datasets into training-ready assets and measurable business outcomes with LDMs.
About NeoSpace
NeoSpace is an innovative startup shaping the future of technology with cutting-edge artificial intelligence solutions. We develop specialized AI models to optimize processes and transform our clients' experience. Our goal is to simplify people's lives and boost business efficiency by creating smarter, more accessible products and services.
What we're looking for
The Data Scientist will be responsible for developing analytical solutions that support strategic decisions — from formulating hypotheses and exploring data to building, validating, and deploying predictive models and advanced analytics. This professional will work closely with multidisciplinary squads, helping raise the organization's analytical maturity.
Responsibilities
- Conduct complex exploratory analyses and identify patterns, anomalies, and business opportunities.
- Develop, validate, and monitor statistical and machine learning models for prediction, classification, recommendation, or optimization.
- Structure, transform, and prepare data from multiple sources, ensuring quality, consistency, and governance.
- Build data and modeling pipelines that promote scalability and reproducibility.
- Collaborate with data engineering, product, and business teams to define requirements, success metrics, and model integrations.
- Design experiments (A/B tests, multivariate tests) and impact analyses.
- Document methodologies, processes, and metrics, ensuring clarity and traceability of solutions.
- Communicate insights clearly, using visualizations and narratives tailored to both technical and non-technical stakeholders.
Requirements
- Solid experience with Python or R for analysis and modeling.
- Proficiency with scientific and machine learning libraries (pandas, NumPy, scikit-learn, statsmodels, etc.).
- Experience with statistical modeling, regression, classification, time series, and validation techniques.
- SQL skills for writing efficient queries against relational databases.
- Experience with version control (Git) and software development best practices.
- Familiarity with distributed computing or cloud environments (AWS, GCP, Azure).
- Knowledge of data visualization (Matplotlib, Seaborn, Plotly, Power BI, or similar).
- Analytical skills and critical thinking.
- Proactive attitude toward exploring hypotheses and proposing solutions.
- Clear verbal and written communication.
- Collaboration with multidisciplinary teams.
- Results-driven mindset with a focus on business value.
- Adaptability in agile, fast-paced environments.
Nice to have
- Experience with deep learning (PyTorch, TensorFlow).
- Hands-on experience with MLOps (MLflow, Kubeflow, SageMaker, Vertex AI).
- Knowledge of modern data architectures (Delta Lake, Lakehouse, Spark).
- Experience deploying models to production (APIs, containers, CI/CD).
- Advanced statistical experimentation practices (causal inference, Bayesian modeling).
- Domain knowledge specific to the company's industry.
Education
- Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or related fields.
- Graduate studies, a master's degree, a PhD, or certifications in Artificial Intelligence and Data Science are a plus.