- Company Name
- Collinson
- Job Title
- Data Scientist
- Job Description
-
**Job title**
Data Scientist
**Role Summary**
Lead end‑to‑end data science initiatives within a global operations and client‑engagement context. Design, build, deploy, and maintain machine‑learning models and scalable data pipelines, translating complex data insights into business‑driven solutions.
**Expectations**
* Apply advanced analytics and ML expertise to solve high‑impact problems.
* Own production‑ready projects from ideation through monitoring.
* Communicate model rationale and results to technical and non‑technical stakeholders.
* Mentor junior scientists and contribute to team knowledge growth.
**Key Responsibilities**
- Build and deploy supervised and unsupervised ML models (REST APIs, batch jobs, FastAPI).
- Design, implement, and maintain scalable data‑science pipelines on AWS.
- Rapidly prototype PoCs and translate them into enterprise‑ready tools.
- Perform exploratory data analysis on structured and unstructured data (customer, transactional, text).
- Manage cloud infrastructure using Docker, Git workflows, and AWS services.
- Create clear, insightful reports and presentations for internal and external audiences.
- Mentor and coach junior data scientists, fostering continuous learning.
**Required Skills**
*Python & SQL*: expert level with NumPy, pandas, scikit‑learn, and SQL dialects.
*Data Handling*: EDA, data wrangling, and visualization using Matplotlib, Plotly, BI tools.
*ML Development*: model building, evaluation, deployment, and monitoring (REST APIs, FastAPI).
*Cloud & DevOps*: AWS experience, Docker, Git, and basic MLOps tools.
*Statistical & ML Foundations*: solid knowledge in statistics, machine‑learning algorithms, and experimental design.
*Communication*: ability to explain technical concepts to non‑technical stakeholders.
*Preferred Technical Add‑ons*
- Deep learning (PyTorch, TensorFlow), transformers, or LLM experience.
- MLOps familiarity (MLflow, SageMaker, Airflow).
- Streaming/dataflow tools (Kafka, Kinesis) and distributed computing (Spark, Dask).
- Data‑visualization frameworks (Streamlit, Dash) and dashboards (Tableau, Looker).
*Soft Skills*
- Curiosity, problem solving, and analytical rigor.
- Self‑starter mindset, thriving in fast‑paced, collaborative environments.
- Strong communicator, business‑savvy, and coaching orientation.
**Required Education & Certifications**
Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related discipline.
Relevant certifications (e.g., AWS Certified Data Analytics, ML‑specific credentials) are a plus.