The race condition occurs because multiple threads can access and modify the shared dictionaries `self.jobs` and `self.results` simultaneously without synchronization. Specifically:
1.  **`add_job`**: Multiple threads might try to add jobs with the same ID at the same time.
2.  **`process_job`**: The "check-then-act" pattern (checking if `job_id` exists, then deleting it) is not atomic. Two threads could check the same job, both proceed to process it, and both attempt to write results or delete the job entry.
3.  **`get_result`**: Reading results while they are being written can lead to inconsistent reads.

To fix this, we introduce a `threading.Lock` to protect all critical sections where shared state is modified or accessed. To ensure high concurrency (allowing the actual processing to happen in parallel), we should only hold the lock while accessing the queue state, not while the `processor` function is executing.

Here is the complete fixed implementation:

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        # Protect the dictionary modification
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # 1. Acquire lock to safely check, retrieve, and remove the job
        with self.lock:
            if job_id not in self.jobs:
                return False
            data = self.jobs[job_id]
            del self.jobs[job_id]
        
        # 2. Process the job OUTSIDE the lock to allow concurrency
        # The lock is released here, allowing other threads to access the queue
        result = processor(data)

        # 3. Acquire lock to safely write the result
        with self.lock:
            self.results[job_id] = result
            
        return True

    def get_result(self, job_id):
        # Protect the read operation for consistency
        with self.lock:
            return self.results.get(job_id)
```

### Key Changes Made:
1.  **Added `self.lock`**: Initialized in `__init__` to create a synchronization primitive.
2.  **Protected `add_job`**: Wrapped the dictionary assignment in a `with self.lock:` block.
3.  **Protected Critical Sections in `process_job`**:
    *   The check for `job_id`, retrieval of `data`, and deletion of `job_id` are now atomic.
    *   The `processor(data)` call is placed **outside** the lock. This is crucial for performance; it allows multiple jobs to be processed in parallel while ensuring the queue state (`jobs` and `results`) remains consistent.
    *   The write to `self.results` is protected by a lock.
4.  **Protected `get_result`**: Wrapped the read operation in a lock to ensure thread-safe access to the results dictionary.