- Company Name
- Solirius Limited
- Job Title
- Senior Data Scientist
- Job Description
-
**Job Title**
Senior Data Scientist (AI Data Scientist)
**Role Summary**
Lead the design, development, deployment, and maintenance of advanced machine‑learning and AI solutions. Work cross‑functionally with product, engineering, and business stakeholders to transform large, heterogeneous data sets into scalable, production‑ready models that deliver measurable business impact. Drive data‑driven decision making by applying statistical analysis, feature engineering, and continuous model improvement.
**Expectations**
- Deliver end‑to‑end AI solutions on time and within budget.
- Maintain high standards of model quality, reproducibility, and scalability.
- Communicate technical findings effectively to technical and non‑technical audiences.
- Stay current with emerging AI/ML technologies and evaluate their relevance to product roadmaps.
- Foster a culture of data ownership, clear documentation, and knowledge sharing within the team.
**Key Responsibilities**
1. Design, prototype, train, evaluate, and deploy predictive, NLP, computer‑vision, and recommender models.
2. Cleanse, transform, and engineer features from large structured and unstructured data sources.
3. Build and maintain scalable data pipelines and automated ML workflows (model training, inference, monitoring).
4. Collaborate with engineering teams to integrate models into production systems and APIs.
5. Monitor model drift, performance, and resource consumption; implement retraining and versioning strategies.
6. Conduct experiments, A/B tests, and statistical analysis to validate model impact.
7. Produce clear, concise reports and visualizations for stakeholders.
8. Evaluate and recommend new AI techniques (LLMs, generative AI, reinforcement learning) for product use cases.
**Required Skills**
- **Machine Learning & AI**: Proven experience with supervised/unsupervised algorithms, deep learning frameworks (TensorFlow, PyTorch), and end‑to‑end model pipelines.
- **Programming**: Advanced proficiency in Python; extensive use of pandas, NumPy, scikit‑learn, and related ML libraries.
- **Data Engineering**: Skilled in building data pipelines with Airflow, Prefect or similar; familiarity with ETL processes.
- **Cloud & MLOps**: Hands‑on experience on AWS, Azure, or GCP; expertise with MLOps tools such as MLflow, Kubeflow, or SageMaker.
- **Databases & Big Data**: Strong SQL skills; working knowledge of NoSQL (MongoDB, Cassandra) and distributed data technologies (Spark, Hive).
- **Statistical Analysis**: Deep understanding of probability, hypothesis testing, and model evaluation metrics.
- **Emerging AI**: Exposure to large language models, prompt engineering, and generative AI tools.
- **Communication & Collaboration**: Excellent written and verbal communication; ability to translate complex analytics into actionable business insights.
- **Version Control & Documentation**: Proficient with Git and well‑maintained code/documentation practices.
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, or related technical field.
- Preferred: Master’s degree or Ph.D. in AI/ML or a closely related discipline.
- Certifications in cloud platforms (AWS Certified Machine Learning – Specialty, Azure AI Engineer Associate, or GCP Professional ML Engineer) are advantageous but not mandatory.