Teamware Solutions is seeking a talented Machine Learning Engineer / Data Scientist with exceptional analytical abilities to join our growing team. You'll play a pivotal role in developing, implementing, and deploying machine learning models that extract actionable insights from complex data, driving intelligent solutions and strategic decision-making across our business. This position is ideal for a data-driven professional passionate about building cutting-edge ML solutions and uncovering hidden patterns in data.
Key Responsibilities
- Machine Learning Model Development:
- Design, develop, and implement machine learning models from conception to deployment.
- Perform feature engineering, model selection, training, tuning, and validation.
- Utilize various ML algorithms (e.g., supervised, unsupervised, reinforcement learning) to solve complex business problems.
- Data Analysis & Preprocessing:
- Conduct in-depth exploratory data analysis (EDA) to understand data characteristics, identify trends, and discover insights.
- Perform data cleaning, transformation, and preprocessing to prepare data for model training.
- Work with large and diverse datasets, ensuring data quality and integrity.
- Problem Solving & Analytical Thinking:
- Translate complex business challenges into quantifiable problems that can be solved using data and machine learning.
- Apply strong analytical and critical thinking skills to interpret model results, validate assumptions, and draw actionable conclusions.
- Propose innovative data-driven solutions and articulate their potential impact to stakeholders.
- Deployment & MLOps:
- Assist in the deployment of ML models into production environments, ensuring scalability, reliability, and performance.
- Monitor model performance post-deployment and implement strategies for continuous improvement and retraining.
- Familiarity with MLOps principles for efficient lifecycle management of ML models.
- Collaboration & Communication:
- Collaborate closely with cross-functional teams including product managers, software engineers, and business analysts.
- Clearly communicate complex analytical findings and technical concepts to both technical and non-technical audiences.
Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
- Proven experience in developing and deploying machine learning models.
- Strong proficiency in Python or R for data analysis and machine learning.
- Expertise with relevant ML libraries/frameworks (e.g., Scikit-learn, TensorFlow, Keras, PyTorch).
- Solid understanding of statistical modeling, hypothesis testing, and experimental design.
- Proficiency in SQL for data extraction and manipulation.
- Demonstrable analytical and problem-solving skills with a keen eye for detail.
- Excellent communication and presentation skills.
Preferred Skills
- Experience with cloud platforms (e.g., AWS SageMaker, Azure ML, Google Cloud AI Platform).
- Familiarity with big data technologies (e.g., Spark, Hadoop).
- Knowledge of data visualization tools (e.g., Tableau, Power BI).
- Experience with version control systems (e.g., Git).
