Data Scientist

Solutions Sync; United States; Remote; USD 124,000 - 193,000; Full time

Job summary

Solutions Sync is seeking a Data Scientist for a remote USA contract focused on predictive analytics, ML, neural networks, and GenAI/LLMs. You will analyze large datasets, build and deploy models, and create PoCs using LLMs to solve business problems.

You will collaborate with data engineers and software teams to productionize models, evaluate performance, and communicate results to stakeholders in a fast-paced environment.

Qualifications

• 5+ years of experience in Data Science, ML, or Applied AI.
• Proficient in Python and data science libraries.
• Hands-on ML model development for classification, regression, forecasting, or clustering.
• Experience with neural networks and deep learning.
• Familiar with PyTorch, TensorFlow, or Keras.
• Knowledge of transformers and NLP.
• Experience with GenAI/LLMs and prompt engineering.
• SQL, Pandas, NumPy, Scikit-learn.
• Cloud experience (AWS/Azure/GCP) and ML pipelines.

Responsibilities

• Analyze large and complex datasets to uncover patterns and business opportunities.
• Develop, optimize, and deploy ML models for business and operational use cases.
• Design neural-network and deep-learning solutions when appropriate.
• Perform feature engineering, data preprocessing, model training, and validation.
• Evaluate models using suitable statistical and ML metrics.
• Run experiments with different algorithms to improve performance.
• Partner with data engineers to build reliable datasets and pipelines.
• Create proof-of-concepts using LLMs and Generative AI for business scenarios.
• Build or contribute to RAG, embeddings, vector search, and prompt-engineering solutions.
• Collaborate with software teams to productionize ML models.
• Monitor model performance and plan retraining strategies.
• Share findings and recommendations with stakeholders.

Skills

ML expertise

GenAI / LLMs

NLP

Python

Model deployment

Experimentation

Data analysis

Education

Master’s degree in CS/DS

Tools

PyTorch

TensorFlow

Keras

Scikit-learn

Pandas

NumPy

Spark

Databricks

Snowflake

Docker

Kubernetes

MLflow

Airflow

Job description

Remote (USA) contract role for a Data Scientist focused on predictive analytics, machine learning, neural networks, and practical GenAI/LLM solutions.

Responsibilities

• Analyze large and complex datasets to uncover patterns, trends, and business opportunities.
• Develop, optimize, and deploy machine learning models for business and operational use cases.
• Design and implement neural-network and deep-learning solutions when appropriate.
• Perform feature engineering, data preprocessing, model training, and validation.
• Evaluate models using suitable statistical and machine-learning metrics.
• Run experiments with different algorithms and architectures to improve model performance.
• Partner with data engineers to build reliable datasets and scalable data pipelines.
• Create proof-of-concepts using LLMs and Generative AI for selected business scenarios.
• Build or contribute to RAG, embeddings, vector search, and prompt-engineering solutions.
• Collaborate with software engineering teams to productionize machine-learning models.
• Monitor model performance and recommend improvements or retraining strategies.
• Share analytical findings, model results, and recommendations with technical and business stakeholders.

Requirements

• 5+ years of experience in Data Science, Machine Learning, or Applied AI.
• Strong programming skills in Python and common data science libraries.
• Hands-on experience building and deploying ML models for classification, regression, clustering, forecasting, or recommendation use cases.
• Strong understanding of supervised and unsupervised learning, including feature engineering, model selection, validation, and performance evaluation.
• Practical experience with neural networks and deep learning.
• Experience with frameworks such as PyTorch, TensorFlow, or Keras.
• Knowledge of neural network architectures including CNNs, RNNs/LSTMs, Transformers, or similar approaches.
• Experience with NLP, text analytics, or unstructured data is highly desirable.
• Some hands-on experience with Generative AI / LLMs, including prompt engineering, embeddings, vector search, RAG, or LLM-based applications.
• Familiarity with GenAI platforms such as OpenAI, Azure OpenAI, AWS Bedrock, Google Vertex AI, or comparable services is a plus.
• Experience with SQL and working with structured and unstructured datasets.
• Experience with Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn, or equivalent tooling.
• Understanding of model evaluation, hyperparameter tuning, cross-validation, and model explainability.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Familiarity with ML pipelines, MLOps, model deployment, and monitoring.
• Ability to translate business problems into measurable ML and data science solutions.

Preferred Qualifications

• Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
• Experience with Transformers, LLMs, NLP, RAG, embeddings, or vector databases.
• Experience with Databricks, Spark, Snowflake, AWS, Azure, or GCP.
• Familiarity with Docker, Kubernetes, MLflow, Airflow, or CI/CD.
• Experience taking machine-learning models from prototype to production.
• Experience in enterprise environments such as financial services, retail, healthcare, technology, or manufacturing.

Technical Stack

• Languages: Python, SQL
• Machine Learning: Scikit-learn, XGBoost, LightGBM
• Deep Learning: PyTorch, TensorFlow, Keras
• Neural Network Architectures: CNNs, RNNs/LSTMs, Transformers
• AI/GenAI: LLMs, prompt engineering, RAG, embeddings, vector search, Transformers
• Data: Pandas, NumPy, Spark, Databricks, Snowflake
• Cloud: AWS, Azure, GCP
• MLOps: MLflow, Docker, Kubernetes, CI/CD
• Visualization: Power BI, Tableau, Matplotlib, Seaborn

Additional Details

• Location: Remote (remote), USA
• Job type: Contract
• Duration: 12+ Months Contract
• Experience range: 5-10 Years
• Core focus: Hands-on experience with traditional machine learning and neural networks, plus practical exposure to GenAI/LLMs