is dedicated to solving complex business problems using advanced data science and engineering solutions. Our interdisciplinary team leverages big data, machine learning, and cloud technologies to deliver actionable insights and impactful products.
:
We are seeking a Senior Data Scientist with strong data engineering skills to drive analytical projects from ideation to deployment. You will design ML models, architect large datasets, build data pipelines, and help deploy scalable solutions in production environments.
Key Responsibilities:
Design, implement, and validate advanced machine learning/statistical models (regression, classification, clustering, NLP, time-series, etc.).
Build and optimize scalable data pipelines for ingestion, cleaning, transformation, and integration from diverse sources (structured, semi-structured, unstructured).
Architect and maintain data warehouses/lakes using cloud platforms (AWS, GCP, Azure).
Collaborate with software engineers, business analysts, and product stakeholders to deliver data-driven solutions.
Analyze large datasets, extract insights, and communicate key findings to technical and non-technical audiences.
Deploy ML models in production using MLOps best practices (Docker, Kubernetes, Airflow, CI/CD).
Mentor junior data scientists and contribute to team knowledge growth.
Stay current with industry trends in data science, engineering, and analytics tools.
Minimum Qualifications:
Master's or PhD in Computer Science, Data Science, Statistics, Mathematics, or related field.
5+ years of experience in data science and data engineering roles.
Deep proficiency in Python and/or R, SQL, and experience with Spark/Hadoop/Databricks or similar big data tools.
Experience designing and automating ETL pipelines (Airflow, Luigi, etc.).
Proven track record in deploying ML models into production environments.
Strong cloud experience (AWS, GCP, or Azure data services).
Advanced knowledge of statistical techniques and machine learning algorithms.
Skilled in data visualization (Tableau, PowerBI, Dash, matplotlib/seaborn).
Preferred Skills:
Experience with NoSQL databases (MongoDB, Cassandra).
Knowledge of containerization and orchestration (Docker, Kubernetes).
Familiarity with data governance, data quality, and security best practices.
Experience working with real-time/streaming data (Kafka, Flink).
Experience with ML model monitoring, retraining, and A/B testing.
Soft Skills:
Strong problem-solving and analytical capabilities.
Excellent communication and stakeholder management skills.
Leadership and mentoring experience.
Detail-oriented with ability to work independently and collaboratively.
Benefits:
Competitive salary and performance bonuses
Flexible remote work options
Cutting-edge projects with cross-functional teams
To Apply:
Please submit your resume/CV, portfolio (GitHub/kaggle/projects), and a cover letter outlining your experience and impact in both data science and data engineering.